evolutionnewsandviews--2020-07-15--Applying Bayes’ Theorem to Biological Design

In a post yesterday , I asked how we can apply the mathematical tool of Bayes ’ Theorem to the case for biological design .
See , “ A Bayesian Approach to Intelligent Design.
” I propose that we construct the argument as follows : Given the hypothesis that living systems are the result of design , it is not extremely unlikely that biological systems would be rich in digitally encoded information content and irreducibly complex machinery ( given that we know from experience with human agents that they often generate information and produce irreducibly complex contraptions ) .
On @ @ @ @ @ @ @ unlikely that such information-rich systems and irreducibly complex machinery could have arisen by an unguided natural process involving chance and physical necessity .
Therefore , given that we in fact find such systems to be abundant in living cells , we may take the presence of such features as strong confirmatory evidence for the hypothesis of design over non-design .
Does such an argument commit a God-of-the-gaps fallacy ?
Not at all .
Considering why can , in fact , be quite instructive for understanding the nature of Bayesian inferences .
As Lydia @ @ @ @ @ @ @ journal Philo , it is easy to conceive of a scenario where we know the likelihood on the hypothesis of chance is very low , but nonetheless we do not have evidence for the likelihood on the hypothesis of design being higher .
For instance , suppose we were to find a small cloud of hydrogen molecules floating in interstellar space in which the molecules were not dispersing .
Without sufficient mass for the cloud to be held together by gravity , such an observation would be an anomaly given our present understanding @ @ @ @ @ @ @ such an observation would be seemingly improbable on the hypothesis of natural law , there would be no reason to think that the hypothesis of design is a better explanation .
After all , there is no independent reason to think that a designer would likely cause a small cloud of hydrogen molecules to clump together .
McGrew further points out that it would be quite a different story if , in the distant future , we were able to capture high resolution images of Alpha Centauri ( the closest star after the @ @ @ @ @ @ @ Beetle was orbiting a planet there .
In that case , the probability of a Volkswagen Beetle being there would be much , much higher on the hypothesis of design than on its falsehood .
No discussion of Bayes ’ Theorem can be complete without a consideration of the prior probability .
Prior probability relates to the intrinsic plausibility of a proposition before the evidence is considered .
Normally the prior probability will be somewhere between zero and one .
A prior probability of one means that the conclusion is certain .
For @ @ @ @ @ @ @ to two is equal to four has a prior probability of one .
It is definitionally true .
A prior probability of zero , conversely , means that the hypothesis entails some sort of logical contradiction ( such as the concept of a married bachelor ) and thus can not be overcome by any amount of evidence .
Priors can be established on the basis of past information .
For example , suppose we want to know the odds that a particular individual won last week ’ s Mega Millions jackpot in the @ @ @ @ @ @ @ be set at 1 in 302.6 million since those are the odds that any individual lottery participant , chosen at random , would win the Mega Millions jackpot .
That is a low prior probability , but it could be overcome if the supposed winner were to subsequently quit his job and start routinely investing in private jets , sports cars , and expensive vacations .
Perhaps he could even show us his bank statement , or the documentary evidence of his winnings .
Those different pieces of evidence , taken together , @ @ @ @ @ @ @ evidence sufficient to overcome a very small prior probability to yield a high posterior probability that the individual did indeed win the Mega Millions jackpot .
In other situations , setting an objective prior is more tricky , and in those cases priors may be determined by a more subjective assessment .
In my own arguments , I tend to set the prior probability generously low , to err on the side of caution .
In the case of ID , however , one could argue that the prior probability of design is @ @ @ @ @ @ @ fine-tuning .
Structuring the argument in the way I have proposed above in fact helps us to address some popular objections to intelligent design .
Tomorrow , I will explain how .

Photo: Mega Millions billboard, by Tony Webster from Minneapolis, Minnesota, United States / CC BY (https://creativecommons.org/licenses/by/2.0).
In a post yesterday, I asked how we can apply the mathematical tool of Bayes’ Theorem to the case for biological design. See, “A Bayesian Approach to Intelligent Design.”
I propose that we construct the argument as follows: Given the hypothesis that living systems are the result of design, it is not extremely unlikely that biological systems would be rich in digitally encoded information content and irreducibly complex machinery (given that we know from experience with human agents that they often generate information and produce irreducibly complex contraptions). On the other hand, it is incredibly unlikely that such information-rich systems and irreducibly complex machinery could have arisen by an unguided natural process involving chance and physical necessity. Therefore, given that we in fact find such systems to be abundant in living cells, we may take the presence of such features as strong confirmatory evidence for the hypothesis of design over non-design.
God of the Gaps?
Does such an argument commit a God-of-the-gaps fallacy? Not at all. Considering why can, in fact, be quite instructive for understanding the nature of Bayesian inferences. As Lydia McGrew explains in her paper in the journal Philo, it is easy to conceive of a scenario where we know the likelihood on the hypothesis of chance is very low, but nonetheless we do not have evidence for the likelihood on the hypothesis of design being higher. For instance, suppose we were to find a small cloud of hydrogen molecules floating in interstellar space in which the molecules were not dispersing. Without sufficient mass for the cloud to be held together by gravity, such an observation would be an anomaly given our present understanding of physics. However, even though such an observation would be seemingly improbable on the hypothesis of natural law, there would be no reason to think that the hypothesis of design is a better explanation. After all, there is no independent reason to think that a designer would likely cause a small cloud of hydrogen molecules to clump together.
McGrew further points out that it would be quite a different story if, in the distant future, we were able to capture high resolution images of Alpha Centauri (the closest star after the sun) and discover that a Volkswagen Beetle was orbiting a planet there. In that case, the probability of a Volkswagen Beetle being there would be much, much higher on the hypothesis of design than on its falsehood.
A Consideration of Prior Probability
No discussion of Bayes’ Theorem can be complete without a consideration of the prior probability. Prior probability relates to the intrinsic plausibility of a proposition before the evidence is considered. Normally the prior probability will be somewhere between zero and one. A prior probability of one means that the conclusion is certain. For instance, the fact that two added to two is equal to four has a prior probability of one. It is definitionally true. A prior probability of zero, conversely, means that the hypothesis entails some sort of logical contradiction (such as the concept of a married bachelor) and thus cannot be overcome by any amount of evidence.
Priors can be established on the basis of past information. For example, suppose we want to know the odds that a particular individual won last week’s Mega Millions jackpot in the United States. The prior probability would be set at 1 in 302.6 million since those are the odds that any individual lottery participant, chosen at random, would win the Mega Millions jackpot. That is a low prior probability, but it could be overcome if the supposed winner were to subsequently quit his job and start routinely investing in private jets, sports cars, and expensive vacations. Perhaps he could even show us his bank statement, or the documentary evidence of his winnings.
Those different pieces of evidence, taken together, would stack up to provide powerful confirmatory evidence sufficient to overcome a very small prior probability to yield a high posterior probability that the individual did indeed win the Mega Millions jackpot. In other situations, setting an objective prior is more tricky, and in those cases priors may be determined by a more subjective assessment. In my own arguments, I tend to set the prior probability generously low, to err on the side of caution. In the case of ID, however, one could argue that the prior probability of design is raised significantly by the evidence of cosmic fine-tuning.
Structuring the argument in the way I have proposed above in fact helps us to address some popular objections to intelligent design. Tomorrow, I will explain how.


evolutionnewsandviews--2020-07-16--Politicized Science Can Be Deadly; Time to Restore Integrity

“ We are being lied to about everything.
” That was the entire anguished text of a tweet that struck me the other day .
It was from a young woman whose top interests , to judge from her Twitter feed , include the science experts ’ response to the COVID-19 pandemic .
It came on the heels of a remarkable public statement from another young woman , Bari Weiss , resigning her job as a journalist at the New York Times .
She explained that she was sickened by the ruling “ orthodoxy ” at the newspaper , a @ @ @ @ @ @ @ , ” “ online venom , ” smears , lies , and vicious false labels directed at the ideologically non-compliant .
Yes , she sounded like she was describing the world of academic Darwinism !
Americans are waking up and recognizing a dangerous lack of integrity in science and in the media .
At a time of crisis , here ’ s what you can do make a positive difference .
Imagine there was a pandemic and a great number of people found themselves greeting statements from scientists , conveyed by the media @ @ @ @ @ @ @ fact , the media at this moment worry about Americans not trusting the “ experts ” about COVID , whether regarding mandatory masks or about the “ fantasy land of a forthcoming return to normalcy , ” as a reporter for The Hill sneered .
Now imagine that , perhaps , what those scientists say really should be believed .
Finally they ’ re telling the truth but half the country won ’ t believe them .
The cost in lives could be staggering .
People distrust the experts because large swaths of @ @ @ @ @ @ @ for advancing an ideological agenda .
We ’ re numb to what they tell us .
Discovery Institute ’ s Center for Science & Culture seeks to restore science to its own credibility .
How ?
By sidestepping the media bullies , to directly challenge the “ experts.
” Our heroes are courageous , truth-telling scientists who upset the smug atheism of the intellectual establishment .
They ask the most important questions of all : Where does life come from ?
Why are we here ?
Two of our heroes have important books @ @ @ @ @ @ @ Institute Press .
Please , we need your help us to publish , distribute , and publicize their work .
Biologist Michael Denton arrives in September with The Miracle of the Cell , detailing the ultra-fine-tuned chemistry , down to the level of atoms , without which the first living cell would be impossible .
Evolution presupposes intelligent design !
Then in November , look out for biochemist Michael Behe with his new book ( not yet titled ) .
One fib told about Behe is that he doesn ’ t respond to @ @ @ @ @ @ @ responding for decades , powerfully , to top ID critics including Kenneth Miller , Richard Dawkins , Sean Carroll , Richard Lenski , and Jerry Coyne .
The new book , close to 500 pages , collects his very best replies .
The search for truth lives on .
These works explain the evidence for purpose and meaning in life against the cynicism and deception of established science .
Please help the world receive this hopeful message at a time when people need it most !
Discovery Institute Press provides vital resources to @ @ @ @ @ @ @ and intelligent design .
You are crucial to our success , as you join hands with our writers , editors , designers , and social media promoters to make these books a reality .

Photo credit: Jon Tyson, via Unsplash.
“We are being lied to about everything.” That was the entire anguished text of a tweet that struck me the other day. It was from a young woman whose top interests, to judge from her Twitter feed, include the science experts’ response to the COVID-19 pandemic.
It came on the heels of a remarkable public statement from another young woman, Bari Weiss, resigning her job as a journalist at the New York Times. She explained that she was sickened by the ruling “orthodoxy” at the newspaper, a “predetermined narrative,” “bullying,” “online venom,” smears, lies, and vicious false labels directed at the ideologically non-compliant.
Yes, she sounded like she was describing the world of academic Darwinism!
A Time of Crisis
Americans are waking up and recognizing a dangerous lack of integrity in science and in the media. At a time of crisis, here’s what you can do make a positive difference.
Imagine there was a pandemic and a great number of people found themselves greeting statements from scientists, conveyed by the media, with increasing jaded skepticism. In fact, the media at this moment worry about Americans not trusting the “experts” about COVID, whether regarding mandatory masks or about the “fantasy land of a forthcoming return to normalcy,” as a reporter for The Hill sneered.
Now imagine that, perhaps, what those scientists say really should be believed. Finally they’re telling the truth but half the country won’t believe them. The cost in lives could be staggering.
People distrust the experts because large swaths of science and the media have traded truth for advancing an ideological agenda. We’re numb to what they tell us.
Discovery Institute’s Center for Science & Culture seeks to restore science to its own credibility. How? By sidestepping the media bullies, to directly challenge the “experts.” Our heroes are courageous, truth-telling scientists who upset the smug atheism of the intellectual establishment. They ask the most important questions of all: Where does life come from? Why are we here?
Denton, Behe: Our Scientist-Heroes
Two of our heroes have important books coming out in the fall from Discovery Institute Press. Please, we need your help us to publish, distribute, and publicize their work. Biologist Michael Denton arrives in September with The Miracle of the Cell, detailing the ultra-fine-tuned chemistry, down to the level of atoms, without which the first living cell would be impossible. Evolution presupposes intelligent design!
Then in November, look out for biochemist Michael Behe with his new book (not yet titled). One fib told about Behe is that he doesn’t respond to critics. Wrong! He has been responding for decades, powerfully, to top ID critics including Kenneth Miller, Richard Dawkins, Sean Carroll, Richard Lenski, and Jerry Coyne. The new book, close to 500 pages, collects his very best replies.
The search for truth lives on. These works explain the evidence for purpose and meaning in life against the cynicism and deception of established science. Please help the world receive this hopeful message at a time when people need it most!
Discovery Institute Press provides vital resources to those searching for the truth about evolution and intelligent design. You are crucial to our success, as you join hands with our writers, editors, designers, and social media promoters to make these books a reality.


evolutionnewsandviews--2020-07-16--Chromosome Dynamics Has Egg-centric Features

In metazoans ranging from arthropods to vertebrates , chromosomes are not inherited as linear , “ beads-on-string ” collections of DNA elements .
Rather , they are bequeathed ( so to speak ) as complex 3D/4D plexuses that are unique not only to the gametes wherein they reside , but also to the earliest “ pre-phases ” of development .
( Indeed , such cytological distinctions as those between euchromatin and heterochromatin often emerge in embryogenesis when , say , gastrulation gets underway , as is the case with Drosophila melanogaster . )
And it is in the egg cell @ @ @ @ @ @ @ taking on exceptional roles , such as becoming the cytoskeletal organizer of the meiotic network or spindle , in addition to providing the sites for centromere/kinetochore assembly , both of which in turn govern the formation of the haploid state that precedes fertilization ( or in some taxa , parthenogenesis ) .
To Arrive at an Egg To begin to see this , we have to take a brief look at certain stages that lead up to a mature egg being produced .
Long before an oocyte can meet with its paternal counterpart @ @ @ @ @ @ @ extensively reorganized .
For instance , the diploid chromosomal set has to be replicated so that the pre-oocyte becomes ( in effect ) tetraploid .
Then synaptonemal complexes are set up between a diploid subset of ( some of ) the chromosomal arms , which are proteinaceous ( or ribonucleoprotein ) scaffoldings that generate DNA-level variability ( among other things ) .
By the pairing-up or synapsis of homologous chromosomes ( bivalents ) , some of the chromatin/DNA is subjected to double-stranded breaks and , hence , to the beginnings of meiotic recombination @ @ @ @ @ @ @ add , the chiasmata ( cross-overs ) that are deployed keep the chromosomes “ interlocked ” for quite some time ( from prophase I to anaphase I ) .
Without these chromatin/DNA bridges from one homologue to its partner and vice versa , meiosis will often result in errors in segregation ( such as non-disjunction ) and transmission that can be unviable for the to-be-formed zygote .
And the importance of maintaining such a physical tethering between homologous chromosomes amidst much of meiosis I , is underscored by the fact that there is @ @ @ @ @ @ @ cross-overs , on the one hand , and the failure of homologous chromosomes or sister chromatids to properly separate , on the other hand ( Baker and Hall 1976 ) .
This brings us to the point that I want to touch on , namely , that of DNA taking on exceptional roles , such as becoming the cytoskeletal organizer of the meiotic spindle .
The bivalents and their chiasmata can determine the events of segregation , but only in the context of dynamic maneuvers that orient properly the homologues to opposite poles @ @ @ @ @ @ @ in the absence of centrosomes — the centriolar-based , microtubule-organizing centers that are necessary for a host of mitotic and non-mitotic processes — as these are eliminated in oogenesis ( for reasons unknown ; Pimenta-Marques , et al .
2016 ; Severson , et al .
2016 ) .
This “ acentrosomal state ” is compensated for , however , by the ability of chromatin/DNA to be involved in the nucleation and organizing of the very meiotic network that is required ( McKim , Hawley 1995 ) .
What this means is that @ @ @ @ @ @ @ prior to and during meiosis I and II , a completely different kind of functionality is superimposed on nucleotide strings — one which goes beyond the erstwhile “ coding ” versus “ non-coding ” distinction .
Without going here into too much detail , based on observations that can be made in vitro , we can note four major events that this functionality governs ( see Radford et al.
, 2017 for most of the following ) .
First , there is the chromatin-/DNA-directed erection of a framework of microtubules around the homologues @ @ @ @ @ @ @ the formation of a gradient of Ran-GTP along the chromosomes and the chromosomal passenger complex ( at least in Xenopus ) , which entails a cascade of initial microtubule-organizing steps .
Second , there is the effecting of a force that generates the hierarchy of the spindle , with two poles becoming established at either end of the central axis .
Third , the chromosomes become dually oriented within the meiotic plexus , as each homologue ( with tight centromeric coupling of its sister chromatids ) is attached to its correct pole .
@ @ @ @ @ @ @ maintained by the cooperative dynamics of multiple microtubule motors , microtubule cross-linkers , regulators of microtubule turnover , and so forth ( Costa , Ohkura , 2019 ) .
Note that upon reaching metaphase I , the process halts…the spindle is maintained all the while ( for days or longer ) until meiosis I resumes with egg activation and/or fertilization .
Thus , what the oocyte bestows to the zygote-to-be is not naked DNA “ replicative strings ” that can be scanned by some Turing Universal-machine , but rather an intricate 3D/4D plexus @ @ @ @ @ @ @ and transmission .
But this is not to say that because “ the chromosomes are more active participants during acentrosomal spindle assembly in oocytes , ” it follows that “ chromosomes carry information that can direct the meiotic divisions ” and that “ chromosomes dictate their own behavior ” ( Radford et al.
, 2017 ) .
Why not ?
Well , in the words of Robert Rosen ( 1984 ) we have to And it is for this reason that I think the acentrosomal meiotic spindle is telling us something profound about @ @ @ @ @ @ @ on the same nucleotide strings , as well as how “ information ” can be sought in its dynamical events .
• Baker B.S.
, Hall J.C. 1976 Meiotic mutants : genetic control of meiotic recombination and chromosome segregation .
In : The Genetics and Biology of Drosophila , edited by Ashburner M. , Novitski E. , pp .
351-434 .
( eds. )
.
Academic Press ; New York .
• Costa M.F.A.
, Ohkura H. 2019 .
The molecular architecture of the meiotic spindle is remodeled during metaphase arrest in oocytes @ @ @ @ @ @ @ 2854-2864.s • Pimenta-Marques A. , Bento I. , Lopes C.A.
, Duarte P. , Jana S.C. , Bettencourt-Dias M. 2016 .
A mechanism for the elimination of the female gamete centrosome in Drosophila melanogaster .
Science 353 : aaf4866 .
• Radford S.J.
, Nguyen A.L.
, Schindler K. , McKim K.S .
2017 .
The chromosomal basis of meiotic acentrosomal spindle assembly and function in oocytes .
Chromosoma 126 : 351-364 .
• Rosen , R. 1984 .
Genomic control of global features of morphogenesis .
In : Modelling of Patterns in @ @ @ @ @ @ @ Jäger et al.
, pp .
318-330 .
Springer Verlag , Berlin , Heidelberg .

Photo: Pig oocyte, by Jakub Friedl / CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0).
In metazoans ranging from arthropods to vertebrates, chromosomes are not inherited as linear, “beads-on-string” collections of DNA elements. Rather, they are bequeathed (so to speak) as complex 3D/4D plexuses that are unique not only to the gametes wherein they reside, but also to the earliest “pre-phases” of development. (Indeed, such cytological distinctions as those between euchromatin and heterochromatin often emerge in embryogenesis when, say, gastrulation gets underway, as is the case with Drosophila melanogaster.) And it is in the egg cell (oocyte) that we see DNA taking on exceptional roles, such as becoming the cytoskeletal organizer of the meiotic network or spindle, in addition to providing the sites for centromere/kinetochore assembly, both of which in turn govern the formation of the haploid state that precedes fertilization (or in some taxa, parthenogenesis).
To Arrive at an Egg
To begin to see this, we have to take a brief look at certain stages that lead up to a mature egg being produced. Long before an oocyte can meet with its paternal counterpart, its nuclear materials have to be extensively reorganized. For instance, the diploid chromosomal set has to be replicated so that the pre-oocyte becomes (in effect) tetraploid. Then synaptonemal complexes are set up between a diploid subset of (some of) the chromosomal arms, which are proteinaceous (or ribonucleoprotein) scaffoldings that generate DNA-level variability (among other things). By the pairing-up or synapsis of homologous chromosomes (bivalents), some of the chromatin/DNA is subjected to double-stranded breaks and, hence, to the beginnings of meiotic recombination. Remarkably now, I hasten to add, the chiasmata (cross-overs) that are deployed keep the chromosomes “interlocked” for quite some time (from prophase I to anaphase I). Without these chromatin/DNA bridges from one homologue to its partner and vice versa, meiosis will often result in errors in segregation (such as non-disjunction) and transmission that can be unviable for the to-be-formed zygote. And the importance of maintaining such a physical tethering between homologous chromosomes amidst much of meiosis I, is underscored by the fact that there is an inverse correlation between the frequency of cross-overs, on the one hand, and the failure of homologous chromosomes or sister chromatids to properly separate, on the other hand (Baker and Hall 1976).
Exceptional Roles for DNA
This brings us to the point that I want to touch on, namely, that of DNA taking on exceptional roles, such as becoming the cytoskeletal organizer of the meiotic spindle. The bivalents and their chiasmata can determine the events of segregation, but only in the context of dynamic maneuvers that orient properly the homologues to opposite poles. Yet the latter has to occur in the absence of centrosomes — the centriolar-based, microtubule-organizing centers that are necessary for a host of mitotic and non-mitotic processes — as these are eliminated in oogenesis (for reasons unknown; Pimenta-Marques, et al. 2016; Severson, et al. 2016). This “acentrosomal state” is compensated for, however, by the ability of chromatin/DNA to be involved in the nucleation and organizing of the very meiotic network that is required (McKim, Hawley 1995). What this means is that along the female germline track, just prior to and during meiosis I and II, a completely different kind of functionality is superimposed on nucleotide strings — one which goes beyond the erstwhile “coding” versus “non-coding” distinction.
Four Major Events
Without going here into too much detail, based on observations that can be made in vitro, we can note four major events that this functionality governs (see Radford et al., 2017 for most of the following). First, there is the chromatin-/DNA-directed erection of a framework of microtubules around the homologues. This is due in part to the formation of a gradient of Ran-GTP along the chromosomes and the chromosomal passenger complex (at least in Xenopus), which entails a cascade of initial microtubule-organizing steps. Second, there is the effecting of a force that generates the hierarchy of the spindle, with two poles becoming established at either end of the central axis. Third, the chromosomes become dually oriented within the meiotic plexus, as each homologue (with tight centromeric coupling of its sister chromatids) is attached to its correct pole. And fourth, the meiotic spindle is maintained by the cooperative dynamics of multiple microtubule motors, microtubule cross-linkers, regulators of microtubule turnover, and so forth (Costa, Ohkura, 2019). Note that upon reaching metaphase I, the process halts…the spindle is maintained all the while (for days or longer) until meiosis I resumes with egg activation and/or fertilization. Thus, what the oocyte bestows to the zygote-to-be is not naked DNA “replicative strings” that can be scanned by some Turing Universal-machine, but rather an intricate 3D/4D plexus that “bootstraps” its own segregation and transmission.
But this is not to say that because “the chromosomes are more active participants during acentrosomal spindle assembly in oocytes,” it follows that “chromosomes carry information that can direct the meiotic divisions” and that “chromosomes dictate their own behavior” (Radford et al., 2017). Why not? Well, in the words of Robert Rosen (1984) we have to
drastically reconsider what is meant by “genetic information,” and…the role of information in modulating dynamical processes like morphogenesis. The fact is that “information” is never manifested statically (e.g. as in a sequence of nucleotides) or represented in terms of configurational coordinates: it always appears in an active role, in the form of rate constants, or more generally in the determination of a vector field imposed on a manifold of underlying configurational variables. Thus, in general, “information” must be characterized in terms of dynamical interactive capability.
And it is for this reason that I think the acentrosomal meiotic spindle is telling us something profound about how different kinds of functionality are superimposed on the same nucleotide strings, as well as how “information” can be sought in its dynamical events.
Literature Cited


evolutionnewsandviews--2020-07-16--Design in Virology? Webinars to Examine COVID-19

The United Kingdom ’ s Centre for Intelligent Design will host a series of YouTube livestreams , beginning this Friday @ @ @ @ @ scientists associated with the intelligent design community .
The theme is “ Design in Virology @ @ @ @ @ will explore the biological and biochemical dimensions of COVID-19 and the implications of what we @ @ @ @ @ The dates , guest speakers , and topics are as follows : • Thursday , @ @ @ @ @ van Woerden on “ Beauty , Coherence , Semiotics , and the Coronavirus ” Each @ @ @ @ @ take place at 3 pm Eastern time ( 8 pm in the U.K. ) .
@ @ @ @ @ after each presentation to ask questions and engage in discussion .
Click the hyperlinks above @ @ @ @ @ livestreams .
A flyer for the events can be downloaded here .

Image credit: Fusion Medical Animation, via Unsplash.
Editor’s note: The link for Dr. Behe’s webinar has been UPDATED.
The United Kingdom’s Centre for Intelligent Design will host a series of YouTube livestreams, beginning this Friday, July 17, featuring scientists associated with the intelligent design community. The theme is “Design in Virology?” and the livestreams will explore the biological and biochemical dimensions of COVID-19 and the implications of what we know about the virus.
The dates, guest speakers, and topics are as follows:
Each of the above events will take place at 3 pm Eastern time (8 pm in the U.K.). There will be an opportunity after each presentation to ask questions and engage in discussion. Click the hyperlinks above to access the respective YouTube livestreams. A flyer for the events can be downloaded here.


evolutionnewsandviews--2020-07-16--The State of the Art in Evolutionary Computation

Editor ’ s note : Dr. Yampolskiy is Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering , University of Louisville .
In this series , he asks : “ What Can and Can ’ t Darwin ’ s Algorithm Compute ?
” See previous posts in the series here : In order to establish the state of the art in evolutionary computation I examined a number of survey papers43 , 44 and seminal results45-50 looking at produced human-competitive results , as they are meant to represent the greatest accomplishments of the @ @ @ @ @ @ @ the results may seem impressive , deeper analysis shows a complete absence of success in evolving non-trivial software from scratch and without human assistance .
It is of course necessary to be precise about what it is we are trying to measure or detect , so as to avoid disagreements resulting from ambiguity in terms being used .
It may be difficult to formally specify what makes a piece of software non-trivial , but an intuitively attractive measure of the length of the program , expressed as the number of lines of code @ @ @ @ @ @ @ complexity , as it could have an extremely low Kolmogorov complexity.51 Inspired by the Turing Test52 , 53 , which is based on an inability to distinguish output from a person and a computer , I propose defining non-trivial software as that which would take an average experienced human programmer at least a full hour of effort to produce if given the same problem to solve .
If the solution source code could be produced with significantly less effort ( for example , one minute ) , it may not be sufficiently complex @ @ @ @ @ @ @ for our purposes .
This approach to specifying triviality would exclude “ Hello World ” and most other toy programs/problems from consideration , which is exactly what we wanted to achieve as the main benefit from being able to evolve software would come from ability to replace full time programmers .
With regard to the other two conditions , they are much easier to specify .
From “ scratch ” means that we are not starting with an existing version of a program ( but are happy to rely on existing APIs , @ @ @ @ @ @ @ produced code ) .
Without human assistance can be interpreted to mean that the programmer is working alone , or a team of programmers is working an equivalent amount of time .
For example , two programmers would each need at least forty minutes to solve the problem , which implies a small communication overhead .
Reading early claims about capabilities of EA feels just like reading early predictions from AI literature54 .
Some early success is projected into the future by assuming that the same rate of progress with continue and it @ @ @ @ @ @ @ years away .
However , as with early AI , the claims are inflated , unsupported , overly optimistic , phrased in deceptive and metaphoric language , and the solutions do not scale to real world problems .
Perhaps an EA “ winter ” is long overdue .
Here is how Koza , presents the state of the field in 1994 : Sixteen years later he reports results of what he calls an “ extraordinarily long ” experiment : One quickly realizes that most improvements in the field simply come from using more @ @ @ @ @ @ @ of the solutions space , a result similar to the one expected for random search algorithm .
Here is an example of overhyped and ambiguous reporting of results from recent work on EA .
Researchers Becker and Gottschlich40 go from naming their paper “ AI Programmer : Autonomously Creating Software Programs Using Genetic Algorithms , ” to the abstract , “ AI Programmer , that can automatically generate full software programs requiring only minimal human guidance , ” to claiming that “ Using AI Programmer , we were able to generate numerous complete @ @ @ @ @ @ @ they state what they managed to produce : “ A generated program that outputs ‘ hello ’ ” or performs addition operation.
” 40 But even that is a bit of a hype : “ Rather than starting with “ Hello World ” , we first had AI Programmer create a more simplistic program that simply output ‘ hi.
’ It was successful after 5,700 generations… ” 40 Even this trivial result was not a clean success .
“ The generated program fulfilled its requirement to output the target text , but interestingly @ @ @ @ @ @ @ parsing errors , including nonmatching brackets.
” 40 An identical program but the one printing “ I love all humans ” took 6,057,200 generations.40 Perhaps it is unfair to pick on this particular paper , which is only available as an unreviewed pre-print , but I selected it because it is highly representative of the type of work frequently published in GP , and its extreme claims make problems clear to identify .
If its title was “ Brute Forcing Strings ” it would be a reasonable work on that subject , but @ @ @ @ @ @ @ claim to be “ Autonomously Creating Software Programs ” using evolutionary computation , a claim that is never substantiated in any published literature on this subject .
Here are similar results from the 2015 “ General Program Synthesis Benchmark Suite ” by Helmuth and Spector : They go on to conclude : • “ We examine what has been achieved in the literature , and find a worrying trend that largely small toy-problems been attempted which require only a few line of code to solve by hand.
” 37 • “ A literature @ @ @ @ @ @ @ fraction of the papers on GP deal with evolving TE computer programs , with the ability to iterate and utilize memory , while the majority of papers deal with evolving less expressive logical or arithmetic functions.
” 37 “ We conclude that GP in its current form is fundamentally awed , when applied to the space of TE programs.
” 37 “ Computer code is not as robust as genetic code , and therefore poorly suited to the process of evolution , resulting in a insurmountable landscape which can not be navigated effectively @ @ @ @ @ @ @ Crossover , has problems being adopted in a computational setting , primarily due to a lack of context of exchanged code .
A review of the literature reveals that evolved programs contain at most two nested loops , indicating that a glass ceiling to what can currently be accomplished.
” 37 • “ There are many problems that traditional Genetic Programming ( GP ) can not solve , due to the theoretical limitations of its paradigm .
A Turing machine ( TM ) is a theoretical abstraction that express the extent of the @ @ @ @ @ @ @ that is Turing complete is sufficiently powerful to recognize all possible algorithms .
GP is not Turing complete.
” 56 Even a survey of the GP community itself produced the following feedback regarding current problems being worked on : • “ Far too many papers include results only on simple toy problems which are often worse than meaningless : they can be misleading ” ; • “ ( [ W ] e should exclude ) irrelevant problems that are at least 20 years old ” ; • “ [ G ] et rid @ @ @ @ @ @ @ ” ; • “ [ T ] he standard ‘ easy ’ Koza set should not be included ” ; In practice GPs are used in the same way as GAs , for optimization of solutions to particular problems or for function optimization36 , 37 , 56-59 or for software improvement.60 • Kannappan , K. , et al.
, Analyzing a decade of human-competitive ( “ humie ” ) winners : What can we learn ?
, in Genetic Programming Theory and Practice XII .
2015 , Springer .
p. 149-166 .
• @ @ @ @ @ @ @ Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes .
PLoS Comput Biol 2008 .
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• Ray , T.S.
, Evolution and optimization of digital organisms .
Scientific Excellence in Supercomputing : The IBM 1990 Contest Prize Papers , 1991 .
• David , O.E.
, et al.
, Genetic algorithms for evolving computer chess programs .
IEEE transactions on evolutionary computation , 2014 .
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• Altshuler , E.E .
and D.S .
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39 ( 2 ) : p. 33-43 .
• Bird , J. and P. Layzell , The evolved radio and its implications for modelling the evolution of novel sensors , in Proceedings of the IEEE Congress on Evolutionary Computation ( CEC ’ 02 ) .
2002. p. 1836-1841 .
• Hauptman , A. and M. Sipper , GP-endchess : Using genetic programming to evolve chess endgame players , in European Conference on Genetic Programming .
2005 , Springer : Berlin , Heidelberg .
p. 120-131 .
@ @ @ @ @ @ @ to the Quantitative Definition of Information .
Problems Inform .
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• Yampolskiy , R.V.
, Turing test as a defining feature of AI-completeness , in Artificial Intelligence , Evolutionary Computing and Metaheuristics .
2013 , Springer Berlin Heidelberg .
p. 3-17 .
• Armstrong , S. and K. Sotala , How we ’ re predicting AI–or failing to , in Beyond Artificial Intelligence .
2015 , Springer .
p. 11-29 .
• Soros , L.B .
and K.O .
Stanley .
@ @ @ @ @ @ @ the artificial life world of chromaria .
in H. Sayama , J. Rieffel , S. Risi , R. Doursat , & H. Lipson ( Eds . )
, Artificial life 14 : Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems .
2014 .
• Teller , A. Turing completeness in the language of genetic programming with indexed memory .
in Evolutionary Computation , 1994 .
IEEE World Congress on Computational Intelligence.
, Proceedings of the First IEEE Conference on .
1994 .
IEEE .
• Woodward , @ @ @ @ @ @ @ A.E .
Brownlee .
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in Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion .
2016 .
ACM .
• Woodward , J.R. GA or GP ?
that is not the question .
in Evolutionary Computation , 2003 .
CEC ’ 03 .
The 2003 Congress on .
2003 .
IEEE .
• Orlov , M. and M. Sipper , Flight of the FINCH through the Java wilderness .
IEEE Transactions on Evolutionary Computation , 2011 @ @ @ @ @ @ @ 166-182 .

Photo: Coming "winter" for EA?, by Fabrice Villard via Unsplash.
Editor’s note: Dr. Yampolskiy is Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. In this series, he asks: “What Can and Can’t Darwin’s Algorithm Compute?” See previous posts in the series here:
In order to establish the state of the art in evolutionary computation I examined a number of survey papers43, 44 and seminal results45-50 looking at produced human-competitive results, as they are meant to represent the greatest accomplishments of the field. While, on the surface the results may seem impressive, deeper analysis shows a complete absence of success in evolving non-trivial software from scratch and without human assistance. It is of course necessary to be precise about what it is we are trying to measure or detect, so as to avoid disagreements resulting from ambiguity in terms being used.
It may be difficult to formally specify what makes a piece of software non-trivial, but an intuitively attractive measure of the length of the program, expressed as the number of lines of code, is not a sufficient indicator of complexity, as it could have an extremely low Kolmogorov complexity.51 Inspired by the Turing Test52, 53, which is based on an inability to distinguish output from a person and a computer, I propose defining non-trivial software as that which would take an average experienced human programmer at least a full hour of effort to produce if given the same problem to solve. If the solution source code could be produced with significantly less effort (for example, one minute), it may not be sufficiently complex and the problem may be deemed trivial for our purposes. This approach to specifying triviality would exclude “Hello World” and most other toy programs/problems from consideration, which is exactly what we wanted to achieve as the main benefit from being able to evolve software would come from ability to replace full time programmers.
Two Further Conditions
With regard to the other two conditions, they are much easier to specify. From “scratch” means that we are not starting with an existing version of a program (but are happy to rely on existing APIs, subject to the non-triviality of all newly produced code). Without human assistance can be interpreted to mean that the programmer is working alone, or a team of programmers is working an equivalent amount of time. For example, two programmers would each need at least forty minutes to solve the problem, which implies a small communication overhead.
Reading early claims about capabilities of EA (Evolutionary Algorithms) feels just like reading early predictions from AI literature54. Some early success is projected into the future by assuming that the same rate of progress with continue and it is claimed that complete success is just years away. However, as with early AI, the claims are inflated, unsupported, overly optimistic, phrased in deceptive and metaphoric language, and the solutions do not scale to real world problems. Perhaps an EA “winter” is long overdue. Here is how Koza, presents the state of the field in 1994:
[I]n this article, we will present a number of examples from various fields supporting the surprising and counter-intuitive notion that computers can indeed by programmed by means of natural selection. We will show, via examples, that the recently developed genetic programming paradigm provides a way to search the space of all possible programs to find a function which solves, or approximately solves, a problem.15
Sixteen years later he reports results of what he calls an “extraordinarily long” experiment:
An additional order-of-magnitude increase was achieved by making extraordinarily long runs on the largest machine (the 1,000-node machine). … The length of the run that produced the two patentable inventions was 28.8 days — almost an order-of-magnitude increase (9.3 times) over the overall 3.4-day average for typical runs of genetic programming that our group had been making at the time.43
One quickly realizes that most improvements in the field simply come from using more computing power to search progressively larger parts of the solutions space, a result similar to the one expected for random search algorithm.
Overhyped and Ambiguous Reporting
Here is an example of overhyped and ambiguous reporting of results from recent work on EA. Researchers Becker and Gottschlich40 go from naming their paper “AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms,” to the abstract, “AI Programmer, that can automatically generate full software programs requiring only minimal human guidance,” to claiming that “Using AI Programmer, we were able to generate numerous complete software programs.” Finally in experimental results they state what they managed to produce: “A generated program that outputs ‘hello’ ” or performs addition operation.”40 But even that is a bit of a hype: “Rather than starting with “Hello World”, we first had AI Programmer create a more simplistic program that simply output ‘hi.’ It was successful after 5,700 generations…”40
Even this trivial result was not a clean success. “The generated program fulfilled its requirement to output the target text, but interestingly included subsequent random characters, which contained parsing errors, including nonmatching brackets.”40 An identical program but the one printing “I love all humans” took 6,057,200 generations.40
Brute Forcing Strings
Perhaps it is unfair to pick on this particular paper, which is only available as an unreviewed pre-print, but I selected it because it is highly representative of the type of work frequently published in GP, and its extreme claims make problems clear to identify. If its title was “Brute Forcing Strings” it would be a reasonable work on that subject, but like so many others, the authors claim to be “Autonomously Creating Software Programs” using evolutionary computation, a claim that is never substantiated in any published literature on this subject.
Here are similar results from the 2015 “General Program Synthesis Benchmark Suite” by Helmuth and Spector:
Of the seven problems on which PushGP found no generalizing solution, most are not surprising in that they involve extensive use of multiple programming constructs, the linking of many distinct steps, or a deceptive fitness space where fitness improvements do not lead toward perfect programs. We have written solutions to each of the unsolved problems by hand …38
They go on to conclude:
While some problems can be solved with programs containing fewer than 10 instructions, none would likely be found using brute force search over our instruction sets within the number of program evaluations allowed here. Searching over size 5 programs using the Number IO instruction set would require evaluating over 7 billion programs, much more than the 5 million we used in our GP runs. Other problems have smallest known solutions of over 20 instructions using instruction sets with more than 100 instructions, to our knowledge beyond the reach of all other program synthesis systems.38
Others have expressed similar skepticism:
“We examine what has been achieved in the literature, and find a worrying trend that largely small toy-problems been attempted which require only a few line of code to solve by hand.” 37
“A full understanding of open-ended evolutionary dynamics remains elusive.” 55
“A literature review has revealed that only a small fraction of the papers on GP deal with evolving TE computer programs, with the ability to iterate and utilize memory, while the majority of papers deal with evolving less expressive logical or arithmetic functions.” 37 “We conclude that GP in its current form is fundamentally awed, when applied to the space of TE programs.” 37 “Computer code is not as robust as genetic code, and therefore poorly suited to the process of evolution, resulting in a insurmountable landscape which cannot be navigated effectively with current syntax based genetic operators. Crossover, has problems being adopted in a computational setting, primarily due to a lack of context of exchanged code. A review of the literature reveals that evolved programs contain at most two nested loops, indicating that a glass ceiling to what can currently be accomplished.” 37
“We conclude that GP in its current form is fundamentally awed, when applied to the space of TE programs.” “Computer code is not as robust as genetic code, and therefore poorly suited to the process of evolution, resulting in a insurmountable landscape which cannot be navigated effectively with current syntax based genetic operators. Crossover, has problems being adopted in a computational setting, primarily due to a lack of context of exchanged code. A review of the literature reveals that evolved programs contain at most two nested loops, indicating that a glass ceiling to what can currently be accomplished.” “There are many problems that traditional Genetic Programming (GP) cannot solve, due to the theoretical limitations of its paradigm. A Turing machine (TM) is a theoretical abstraction that express the extent of the computational power of algorithms. Any system that is Turing complete is sufficiently powerful to recognize all possible algorithms. GP is not Turing complete.”56
“Time to Move On”
Even a survey of the GP community itself produced the following feedback regarding current problems being worked on:
“Far too many papers include results only on simple toy problems which are often worse than meaningless: they can be misleading”;
“([W]e should exclude) irrelevant problems that are at least 20 years old”;
“[G]et rid of some outdated, too easy benchmarks”;
“[T]he standard ‘easy’ Koza set should not be included”;
“[It is] time to move on.”36
In practice GPs are used in the same way as GAs, for optimization of solutions to particular problems or for function optimization36, 37, 56-59 or for software improvement.60
Tomorrow, “Possible Reasons for Limited Success of Evolutionary Algorithms.”
References:


evolutionnewsandviews--2020-07-17--Some Possible Reasons for the Limited Success of Evolutionary Algorithms

Editor ’ s note : Dr. Yampolskiy is Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering , University of Louisville .
In this series , he asks : “ What Can and Can ’ t Darwin ’ s Algorithm Compute ?
” See previous posts in the series here : • “ The State of the Art in Evolutionary Computation ” A number of possible explanations for “ Why don ’ t we evolve software ?
” could be considered .
I have tried to be as comprehensive as possible below @ @ @ @ @ @ @ have not considered some plausible explanations .
• Incompetent programmers .
It is theoretically possible , but highly unlikely , that out of thousands of scientists working on evolutionary computation all failed to correctly implement the Darwinian algorithm .
• Non-representative algorithms .
Some56 have suggested that evolutionary algorithms do not accurately capture the theory of evolution , but of course , that would imply that the theory itself is not specified in sufficient detail to make falsifiable predictions .
If , on the other hand , such more detailed specifications are available @ @ @ @ @ @ @ to them to implement them as computer simulations for testing purposes .
However , no successful examples of such work is known and the known ones have not been successful in evolving software .
• Inadequate fitness functions .
Fitness function for a complex software product is difficult to outline and specify and may be as complex as ( or even more complex than ) the software we want to evolve as it has to consider all the possible use cases and pass all unit tests .
This may be the Achilles heel @ @ @ @ @ @ @ an objection to the feasibility of programming in general and GP in particular , as both have to convert software specification into the source code .
If human programmers and biological evolution succeed with such constraints , so should Darwinian simulations .
• The Halting problem .
Alan Turing proved61 that it is impossible to determine if an arbitrary program halts , but this is also a problem for human programmers and could be address easily by placing time limits on considered solutions .
• Program correctness .
If we require evolved software @ @ @ @ @ @ @ a problem as GP doesn ’ t verify produced designs , but only tests them against specific unit tests .
Likewise , we can ’ t rely on automated software verification as it is still an unsolved problem4 in the general case .
This is not really a problem as a majority of human-written software is never proven to be correct and only a small portion of software engineering process relies of formal specification and Test Driven Development .
• Inappropriate solutions .
Literature on EA is full of examples62 of the surprising @ @ @ @ @ @ @ that match the letter of design specifications but not the spirit .
This is similar to human-produced software and numerous examples of ways in which such software fails the goals of the initial design.63 • Insufficient complexity of the environment ( not enough data , poor fitness functions ) .
It is possible that the simulated environment is not complex enough to generate high complexity outputs in evolutionary simulations .
This does not seem correct as the Internet presents a highly complex landscape in which many self-modifying computer viruses roam.64 Likewise , a @ @ @ @ @ @ @ many others present close approximations to the real world and are certainly more complex than the early Earth was .
“ A skeptic might insist that an abstract environment would be inadequate for the evolution … , believing instead that the virtual environment would need to closely resemble the actual biological environment in which our ancestors evolved .
Creating a physically realistic virtual world would require a far greater investment of computational resources than the simulation of a simple toy world or abstract problem domain ( whereas evolution had access to a physically @ @ @ @ @ @ @ ) .
In the limiting case , if complete microphysical accuracy were insisted upon , the computational requirements would balloon to utterly infeasible proportions.
” 39 Requiring more realistic environmental conditions may results in an increase in necessary computational resources , a problem addressed in the next bullet .
Eiben and Smith suggest that evolvable hardware may provide complexity which is currently missing from software-only emulations : “ The use of real hardware overcomes the principal deficiency of software models , which lack the richness of matter that is a source of challenges @ @ @ @ @ @ @ algorithms.
” 41 • Insufficient resources ( Compute , Memory ) .
From the history of computer science , we know of many situations ( speech recognition , NN training ) , where we had a correct algorithm , but insufficient computational resources to run it to success .
It is possible that we simply don ’ t have hardware powerful enough to emulate evolution .
I will address this possibility in my next post .
• Software design is not amenable to evolutionary methods .
The space of software designs may be @ @ @ @ @ @ @ fitness to the desired solutions .
This is possible , but it implies that the original goals of GP are unattainable and misguided .
In addition , since a clear mapping exists between solutions to problems and animals as solutions to environmental problems this would also imply that the current explanation for the origin of the species is incorrect.65 • Darwinian algorithm is incomplete or wrong .
Lastly , we have to consider the possibility that the inspiration behind evolutionary computation , the Darwinian algorithm itself , is wrong or at least incomplete @ @ @ @ @ @ @ simulations of such an algorithm would fail to produce results comparable to observations we see in nature and a search for an alternative algorithm would need to take place .
This would be an extraordinary claim and would require that we discard all the other possible explanations from this list .
Perhaps , we can learn some answers from similar historical conundrums .
The earliest work on artificial neurons was done in 1943 by McCulloch and Pitts66 and while research on Artificial Neural Networks ( ANN ) continued67 , until 2010 it would @ @ @ @ @ @ @ “ Why don ’ t artificial neural networks perform as well as natural ones ?
” Today , deep neural networks frequently outperform their human counterparts68 , 69 .
But it may still be helpful to answer this question about NN , to see how it was resolved .
Stuhlmüller succinctly summarizes the answer given by Ghahramani : “ Why does deep learning work now , but not 20 years ago , even though many of the core ideas were there ?
In one sentence : We have more data , more compute @ @ @ @ @ @ @ few algorithmic innovations … ” 70 • Turing , A. , On computable numbers , with an application to the Entscheidungsproblem .
Proceedings of the London Mathematical Society , 1936 .
2 ( 42 ) : p. 230-265 .
• Lehman , J. , et al.
, The surprising creativity of digital evolution : A collection of anecdotes from the evolutionary computation and artificial life research communities .
arXiv preprint arXiv:1803.03453 , 2018 .
• Yampolskiy , R.V .
and M. Spellchecker , Artificial Intelligence Safety and Cybersecurity : a Timeline of AI @ @ @ @ @ @ @ .
• Nachenberg , C. , Computer virus-antivirus coevolution .
Communications of the ACM , 1997 .
40 ( 1 ) : p. 46-51 .
• Yampolskiy , R.V.
, On the origin of synthetic life : attribution of output to a particular algorithm .
Physica Scripta , 2016 .
92 ( 1 ) : p. 013002 .
• McCulloch , W.S .
and W. Pitts , A logical calculus of the ideas immanent in nervous activity .
The bulletin of mathematical biophysics , 1943 .
5 ( 4 ) : p. 115-133 @ @ @ @ @ @ @ things I learned at NIPS 2016 , in NIPS .
2016 : Available at : https : //blog.ought.com/nips-2016-875bb8fadb8c .

Photo credit: Arif Riyanto via Unsplash.
Editor’s note: Dr. Yampolskiy is Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. In this series, he asks: “What Can and Can’t Darwin’s Algorithm Compute?” See previous posts in the series here:
A number of possible explanations for “Why don’t we evolve software?” could be considered. I have tried to be as comprehensive as possible below, but it is possible that I have not considered some plausible explanations.
Some Possible Explanations
Incompetent programmers . It is theoretically possible, but highly unlikely, that out of thousands of scientists working on evolutionary computation all failed to correctly implement the Darwinian algorithm.
. It is theoretically possible, but highly unlikely, that out of thousands of scientists working on evolutionary computation all failed to correctly implement the Darwinian algorithm. Non-representative algorithms . Some 56 have suggested that evolutionary algorithms do not accurately capture the theory of evolution, but of course, that would imply that the theory itself is not specified in sufficient detail to make falsifiable predictions. If, on the other hand, such more detailed specifications are available to GP believers, it is up to them to implement them as computer simulations for testing purposes. However, no successful examples of such work is known and the known ones have not been successful in evolving software.
. Some have suggested that evolutionary algorithms do not accurately capture the theory of evolution, but of course, that would imply that the theory itself is not specified in sufficient detail to make falsifiable predictions. If, on the other hand, such more detailed specifications are available to GP believers, it is up to them to implement them as computer simulations for testing purposes. However, no successful examples of such work is known and the known ones have not been successful in evolving software. Inadequate fitness functions . Fitness function for a complex software product is difficult to outline and specify and may be as complex as (or even more complex than) the software we want to evolve as it has to consider all the possible use cases and pass all unit tests. This may be the Achilles heel of GP, but it is also an objection to the feasibility of programming in general and GP in particular, as both have to convert software specification into the source code. If human programmers and biological evolution succeed with such constraints, so should Darwinian simulations.
. Fitness function for a complex software product is difficult to outline and specify and may be as complex as (or even more complex than) the software we want to evolve as it has to consider all the possible use cases and pass all unit tests. This may be the Achilles heel of GP, but it is also an objection to the feasibility of programming in general and GP in particular, as both have to convert software specification into the source code. If human programmers and biological evolution succeed with such constraints, so should Darwinian simulations. The Halting problem . Alan Turing proved 61 that it is impossible to determine if an arbitrary program halts, but this is also a problem for human programmers and could be address easily by placing time limits on considered solutions.
. Alan Turing proved that it is impossible to determine if an arbitrary program halts, but this is also a problem for human programmers and could be address easily by placing time limits on considered solutions. Program correctness . If we require evolved software to be provably correct this would present a problem as GP doesn’t verify produced designs, but only tests them against specific unit tests. Likewise, we can’t rely on automated software verification as it is still an unsolved problem 4 in the general case. This is not really a problem as a majority of human-written software is never proven to be correct and only a small portion of software engineering process relies of formal specification and Test Driven Development.
. If we require evolved software to be provably correct this would present a problem as GP doesn’t verify produced designs, but only tests them against specific unit tests. Likewise, we can’t rely on automated software verification as it is still an unsolved problem in the general case. This is not really a problem as a majority of human-written software is never proven to be correct and only a small portion of software engineering process relies of formal specification and Test Driven Development. Inappropriate solutions . Literature on EA is full of examples 62 of the surprising creativity of Darwinian algorithms resulting in solutions that match the letter of design specifications but not the spirit. This is similar to human-produced software and numerous examples of ways in which such software fails the goals of the initial design. 63
. Literature on EA is full of examples of the surprising creativity of Darwinian algorithms resulting in solutions that match the letter of design specifications but not the spirit. This is similar to human-produced software and numerous examples of ways in which such software fails the goals of the initial design. Insufficient complexity of the environment (not enough data, poor fitness functions) . It is possible that the simulated environment is not complex enough to generate high complexity outputs in evolutionary simulations. This does not seem correct as the Internet presents a highly complex landscape in which many self-modifying computer viruses roam. 64 Likewise, a virtual world such as Second Life and many others present close approximations to the real world and are certainly more complex than the early Earth was. “A skeptic might insist that an abstract environment would be inadequate for the evolution …, believing instead that the virtual environment would need to closely resemble the actual biological environment in which our ancestors evolved. Creating a physically realistic virtual world would require a far greater investment of computational resources than the simulation of a simple toy world or abstract problem domain (whereas evolution had access to a physically realistic real world ‘for free’). In the limiting case, if complete microphysical accuracy were insisted upon, the computational requirements would balloon to utterly infeasible proportions.” 39 Requiring more realistic environmental conditions may results in an increase in necessary computational resources, a problem addressed in the next bullet. Eiben and Smith suggest that evolvable hardware may provide complexity which is currently missing from software-only emulations: “The use of real hardware overcomes the principal deficiency of software models, which lack the richness of matter that is a source of challenges and opportunities not yet matched in artificial algorithms.” 41
. It is possible that the simulated environment is not complex enough to generate high complexity outputs in evolutionary simulations. This does not seem correct as the Internet presents a highly complex landscape in which many self-modifying computer viruses roam. Likewise, a virtual world such as Second Life and many others present close approximations to the real world and are certainly more complex than the early Earth was. “A skeptic might insist that an abstract environment would be inadequate for the evolution …, believing instead that the virtual environment would need to closely resemble the actual biological environment in which our ancestors evolved. Creating a physically realistic virtual world would require a far greater investment of computational resources than the simulation of a simple toy world or abstract problem domain (whereas evolution had access to a physically realistic real world ‘for free’). In the limiting case, if complete microphysical accuracy were insisted upon, the computational requirements would balloon to utterly infeasible proportions.” Requiring more realistic environmental conditions may results in an increase in necessary computational resources, a problem addressed in the next bullet. Eiben and Smith suggest that evolvable hardware may provide complexity which is currently missing from software-only emulations: “The use of real hardware overcomes the principal deficiency of software models, which lack the richness of matter that is a source of challenges and opportunities not yet matched in artificial algorithms.” Insufficient resources (Compute, Memory) . From the history of computer science, we know of many situations (speech recognition, NN training), where we had a correct algorithm, but insufficient computational resources to run it to success. It is possible that we simply don’t have hardware powerful enough to emulate evolution. I will address this possibility in my next post.
. From the history of computer science, we know of many situations (speech recognition, NN training), where we had a correct algorithm, but insufficient computational resources to run it to success. It is possible that we simply don’t have hardware powerful enough to emulate evolution. I will address this possibility in my next post. Software design is not amenable to evolutionary methods . The space of software designs may be discrete with no continuous path via incremental fitness to the desired solutions. This is possible, but it implies that the original goals of GP are unattainable and misguided. In addition, since a clear mapping exists between solutions to problems and animals as solutions to environmental problems this would also imply that the current explanation for the origin of the species is incorrect. 65
. The space of software designs may be discrete with no continuous path via incremental fitness to the desired solutions. This is possible, but it implies that the original goals of GP are unattainable and misguided. In addition, since a clear mapping exists between solutions to problems and animals as solutions to environmental problems this would also imply that the current explanation for the origin of the species is incorrect. Darwinian algorithm is incomplete or wrong. Lastly, we have to consider the possibility that the inspiration behind evolutionary computation, the Darwinian algorithm itself, is wrong or at least incomplete. If that were true, computer simulations of such an algorithm would fail to produce results comparable to observations we see in nature and a search for an alternative algorithm would need to take place. This would be an extraordinary claim and would require that we discard all the other possible explanations from this list.
Lessons from History
Perhaps, we can learn some answers from similar historical conundrums. The earliest work on artificial neurons was done in 1943 by McCulloch and Pitts66 and while research on Artificial Neural Networks (ANN) continued67, until 2010 it would have been very logical to ask: “Why don’t artificial neural networks perform as well as natural ones?” Today, deep neural networks frequently outperform their human counterparts68, 69. But it may still be helpful to answer this question about NN, to see how it was resolved. Stuhlmüller succinctly summarizes the answer given by Ghahramani: “Why does deep learning work now, but not 20 years ago, even though many of the core ideas were there? In one sentence: We have more data, more compute, better software engineering, and a few algorithmic innovations …”70
Tomorrow, “Neuroevolution Methods Show Significant Success.”
References:


evolutionnewsandviews--2020-07-17--Why Science and Atheism Don’t Mix

Why Science and Atheism Don’t Mix

Editor ’ s note : This article is an excerpt from John Lennox ’ s new book , 2084 : Artificial Intelligence and the Future of Humanity .
Dr. Lennox is Professor of Mathematics at the University of Oxford .
Science proceeds on the basis of the assumption that the universe is , at least to a certain extent , accessible to the human mind .
No science can be done without the scientist believing this , so it is important to ask for grounds for this belief .
Atheism gives us none , since it posits a mindless , @ @ @ @ @ @ @ life and consciousness .
Charles Darwin saw the problem .
He wrote : “ With me the horrid doubt always arises whether the convictions of man ’ s mind , which has been developed from the mind of the lower animals , are of any value or at all trustworthy.
” Similarly , physicist John Polkinghorne says that the reduction of mental events to physics and chemistry destroys meaning : Thought is replaced by electrochemical neural events .
Two such events can not confront each other in rational discourse .
They are neither @ @ @ @ @ @ @ .
.
.
The world of rational discourse dissolves into the absurd chatter of firing synapses .
Quite frankly that can not be right and none of us believes it to be so .
Polkinghorne is a Christian , but some well-known atheists also acknowledge the difficulty here .
In his book Mind and Cosmos , leading atheist philosopher Thomas Nagel says : That is , naturalism , and therefore atheism , undermines the foundations of the very rationality that is needed to construct or understand or believe in any kind of argument @ @ @ @ @ @ @ .
In short , it leads to the abolition of reason — a kind of “ abolition of man , ” since reason is an essential part of what it means to be human .
Not surprisingly , I reject atheism because I believe Christianity to be true .
But that is not my only reason .
I also reject it because I am a mathematician interested in science and rational thought .
How could I espouse a worldview that arguably abolishes the very rationality I need to do mathematics ?
By contrast @ @ @ @ @ @ @ origin of human rationality to the fact that we are created in the image of a rational God makes real sense as an explanation of why we can do science .
Science and God mix very well .
It is science and atheism that do not mix .

Photo: Clouds of Jupiter, by Gerald Eichstädt and Sean Doran (CC BY-NC-SA)/NASA/JPL-Caltech/SwRI/MSSS.
Editor’s note: This article is an excerpt from John Lennox’s new book, 2084: Artificial Intelligence and the Future of Humanity. Dr. Lennox is Professor of Mathematics at the University of Oxford.
Science proceeds on the basis of the assumption that the universe is, at least to a certain extent, accessible to the human mind. No science can be done without the scientist believing this, so it is important to ask for grounds for this belief. Atheism gives us none, since it posits a mindless, unguided origin of the universe’s life and consciousness.
Charles Darwin saw the problem. He wrote: “With me the horrid doubt always arises whether the convictions of man’s mind, which has been developed from the mind of the lower animals, are of any value or at all trustworthy.”
Similarly, physicist John Polkinghorne says that the reduction of mental events to physics and chemistry destroys meaning:
Thought is replaced by electrochemical neural events. Two such events cannot confront each other in rational discourse. They are neither right nor wrong. They simply happen . . . The world of rational discourse dissolves into the absurd chatter of firing synapses. Quite frankly that cannot be right and none of us believes it to be so.
Polkinghorne is a Christian, but some well-known atheists also acknowledge the difficulty here.
The Abolition of Reason
In his book Mind and Cosmos, leading atheist philosopher Thomas Nagel says:
If the mental is not itself merely physical, it cannot be fully explained by physical science . . . Evolutionary naturalism implies that we should not take any of our convictions seriously, including the scientific world picture on which evolutionary naturalism depends.
That is, naturalism, and therefore atheism, undermines the foundations of the very rationality that is needed to construct or understand or believe in any kind of argument whatsoever, let alone a scientific one. In short, it leads to the abolition of reason — a kind of “abolition of man,” since reason is an essential part of what it means to be human.
Not surprisingly, I reject atheism because I believe Christianity to be true. But that is not my only reason. I also reject it because I am a mathematician interested in science and rational thought. How could I espouse a worldview that arguably abolishes the very rationality I need to do mathematics? By contrast, the biblical worldview that traces the origin of human rationality to the fact that we are created in the image of a rational God makes real sense as an explanation of why we can do science.
Science and God mix very well. It is science and atheism that do not mix.


evolutionnewsandviews--2020-07-17--COVID-19 and Biochemical Design

COVID-19 and Biochemical Design

Editor ’ s note : The link for Dr. Behe ’ s webinar has been UPDATED .
The COVID-19 pandemic @ @ @ @ @ Just what are viruses ?
How do they work ?
Can they easily evolve to @ @ @ @ @ This afternoon at 3 pm Eastern Daylight Time , I will present the first lecture @ @ @ @ @ by the British Centre for Intelligent Design .
You can join me by clicking here @ @ @ @ @ in historical time , their basic biochemical design , where they came from , and @ @ @ @ @ .

Photo credit: Tai's Captures, via Unsplash.
Editor’s note: The link for Dr. Behe’s webinar has been UPDATED.
The COVID-19 pandemic has many people wondering: Just what are viruses? How do they work? Can they easily evolve to become even more deadly? This afternoon at 3 pm Eastern Daylight Time, I will present the first lecture in a three-part series sponsored by the British Centre for Intelligent Design. You can join me by clicking here. I will discuss viruses in historical time, their basic biochemical design, where they came from, and what they might evolve into.


evolutionnewsandviews--2020-07-18--Neuroevolution Methods Show Significant Success

Editor ’ s note : Dr. Yampolskiy is Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering , University of Louisville .
In this series , concluding today , he asks : “ What Can and Can ’ t Darwin ’ s Algorithm Compute ?
” See previous posts in the series here : • “ The State of the Art in Evolutionary Computation ” • “ Some Possible Reasons for the Limited Success of Evolutionary Algorithms ” My analysis of the relevant literature shows that no one has succeeded at evolving @ @ @ @ @ @ @ words , the Darwinian algorithm works in theory , but does not work in practice , when applied in the domain of software production .
The reason we do not evolve software is that the space of working programs is very large and discrete .
While hill-climbing-heuristic-based evolutionary computations are excellent at solving many optimization problems , they fail in the domains of non-continuous fitness.71 This is also the reason we do not evolve complex life or novel engineering designs .
We can conclude that 1 ) Simulations of evolution do not produce @ @ @ @ @ @ @ evolutionary algorithms longer leads to progressively diminishing results .
Others have come to similar conclusions : Genetic algorithms do not scale well with complexity .
That is , where the number of elements which are exposed to mutation is large there is often an exponential increase in search space size .
This makes it extremely difficult to use the technique on problems such as designing an engine , a house or plane .
In order to make such problems tractable to evolutionary search , they must be broken down into the simplest representation @ @ @ @ @ @ @ algorithms encoding designs for fan blades instead of engines , building shapes instead of detailed construction plans , and airfoils instead of whole aircraft designs .
The second problem of complexity is the issue of how to protect parts that have evolved to represent good solutions from further destructive mutation , particularly when their fitness assessment requires them to combine well with other parts.76 Even John Koza himself acknowledges that it would be highly surprising if his approach could work : My Challenge to the EA Community I challenge the EA community to @ @ @ @ @ @ @ that evolves non-trivial code from scratch and without human help .
That would be the only way in which my findings could be shown to be incorrect .
On the positive side , the fact that it seems impossible to evolve complex code implies that we are unlikely to be able to directly evolve highly sophisticated artificially intelligent agents , which may present significant risk to our safety and security.77-83 Just imagine what would have happened if the very first time we ran a simulation of evolution on a computer , it produced @ @ @ @ @ @ @ that Programming as a problem is AI Complete84 : if GP can solve Programming that would imply that GP = AGI ( Artificial General Intelligence ) .
But we see no experimental evidence for such a claim .
In fact , it is more likely that once we have AGI , it could be used to create an intelligent fitness function for GP and so make it possible to evolve code .
Genetic Programming will not be the cause of Artificial Intelligence , but a product of it .
On the Other Hand @ @ @ @ @ @ @ methods for optimizing neural networks ( weights , parameters and deep learning architectures ) show significant success in a number of domains and remain a strong contender for the creation of AGI.85 Karpathy suggests that neural networks represent the future of software engineering , since they allow the substitution of software based on difficult-to-write source code with automated optimization based on code evaluation : The “ classical stack ” of Software 1.0 is what we ’ re all familiar with — it is written in languages such as Python , C++ , etc @ @ @ @ @ @ @ the computer written by a programmer .
By writing each line of code , the programmer identifies a specific point in program space with some desirable behavior .
…In contrast , Software 2.0 can be written in much more abstract , human unfriendly language , such as the weights of a neural network .
No human is involved in writing this code because there are a lot of weights ( typical networks might have millions ) … Instead , our approach is to specify some goal on the behavior of a desirable program @ @ @ @ @ @ @ of input output pairs of examples ” , or “ win a game of Go ” ) , write a rough skeleton of the code ( e.g .
a neural net architecture ) , that identifies a subset of program space to search , and use the computational resources at our disposal to search this space for a program that works .
In the specific case of neural networks , we restrict the search to a continuous subset of the program space where the search process can be made ( somewhat surprisingly ) @ @ @ @ @ @ @ turns out that a large portion of real-world problems have the property that it is significantly easier to collect the data ( or more generally , identify a desirable behavior ) than to explicitly write the program….Software 1.0 is code we write .
Software 2.0 is code written by the optimization based on an evaluation criterion ( such as “ classify this training data correctly ” ) .
It is likely that any setting where the program is not obvious but one can repeatedly evaluate the performance of it ( e.g .
— @ @ @ @ @ @ @ do you win games of Go ? )
will be subject to this transition , because the optimization can find much better code than what a human can write.86 Previously , a number of attempts have been made to understand what makes certain problems hard in the context of evolutionary algorithms87 , typically taking inspiration from computational complexity and subdividing all problems into GA-Easy ( search space grows linearly with the size of input ) and GA-Hard ( search space grows exponentially with the size of input ) for the problem encoded in @ @ @ @ @ @ @ , in their quest to explain Hardness , have looked at the presence in the search space of deceptive attractors which guide the search away from global maxima.90 I , likewise , suggest the existence of two broad classes of problems , based on whatever we are optimizing an existing design or trying to discover a novel design .
If a particular class of problems can be represented as an optimization problem with a smooth fitness landscape , it is likely to be solvable by EAs .
However , most software engineering/design problems @ @ @ @ @ @ @ and so are resistant to the evolutionary hill-climbing paradigm , with GP not able to outperform random search on such problems .
Whether it is possible to convert such design problems into optimization problems remains an open questions and is similar in difficulty and consequences to the famous P VS NP91 problem .
While it seems unlikely that evolutionary algorithms will be able to solve design problems directly , it may be possible to use them to evolve advanced artificial intelligences ( Darwin Machines92 in evolvable hardware93 , 94 ) via neuroevolution .
@ @ @ @ @ @ @ to solve design problems .
Stanley et al .
also argue that neuroevolution may be a likely path to strong artificial intelligence That means any safety concerns we have about advanced artificial intelligence remain as an unfortunate side-effect of research into neuroevolution .
They are not eliminated due to the fundamental limits of the Darwinian algorithm .
An earlier version of this series was published as “ Why We Do Not Evolve Software ?
Analysis of Evolutionary Algorithms.
” Evolutionary Bioinformatics .
Volume 14 : 1–11 .
2018 .
© Roman V. Yampolskiy @ @ @ @ @ @ @ al.
, A Study on the Limitations of Evolutionary Computation and other Bio-inspired Approaches for Integer Factorization .
Procedia Computer Science , 2015 .
62 : p. 603-610 .
• Hsu , F.-H. , Behind Deep Blue : Building the computer that defeated the world chess champion .
2004 : Princeton University Press .
• Silver , D. , et al.
, Mastering the game of Go with deep neural networks and tree search .
nature , 2016 .
529 ( 7587 ) : p. 484-489 .
• Silver , D. , et @ @ @ @ @ @ @ without human knowledge .
Nature , 2017 .
550 ( 7676 ) : p. 354 .
• Dijkstra , E.W.
, On the cruelty of really teaching computing science .
Communications of the ACM , 1989 .
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• Mewada , S. , P. Sharma , and S. Gautam , Exploration of Fuzzy System With Applications , in Handbook of Research on Promoting Business Process Improvement Through Inventory Control Techniques .
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• Babcock , J. , J. Kramár @ @ @ @ @ @ @ containment problem .
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Unethical Research : How to Create a Malevolent Artificial Intelligence .
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• Ramamoorthy , A. and R. Yampolskiy , Beyond Mad ?
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, AI-Complete , AI-Hard , or AI-Easy – Classification of Problems in AI , in The 23rd Midwest Artificial Intelligence and Cognitive Science Conference .
April 21-22 , 2012 @ @ @ @ @ @ @ • Karpathy , A. , Software 2.0 , in Medium .
November 11 , 2017 : Available at : https : //medium.com/ @ karpathy/software-2-0-a64152b37c35 .
• Eiben , Á. , P.-E. Raué , and Z. Ruttkay , GA-easy and GA-hard constraint satisfaction problems , in Constraint Processing .
1995 , Springer .
p. 267-283 .
• Rylander , B. and J .
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in WSEAS International Conference on Evolutionary Computation , Tenerife Playa , Canary Islands , Spain .
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• Forrest , @ @ @ @ @ @ @ a problem hard for a genetic algorithm ?
Some anomalous results and their explanation .
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• de Garis , H. Evolvable hardware genetic programming of a Darwin machine .
in Artificial neural nets and genetic algorithms .
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• Higuchi , T. , et al .
Evolving hardware with genetic learning : A first step towards building a Darwin machine .
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111 ( 2-3 ) : p. 163-192 .

Image credit: mikemacmarketing / photo on flickr / CC BY (https://creativecommons.org/licenses/by/2.0).
Editor’s note: Dr. Yampolskiy is Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. In this series, concluding today, he asks: “What Can and Can’t Darwin’s Algorithm Compute?” See previous posts in the series here:
My analysis of the relevant literature shows that no one has succeeded at evolving non-trivial software from scratch. In other words, the Darwinian algorithm works in theory, but does not work in practice, when applied in the domain of software production. The reason we do not evolve software is that the space of working programs is very large and discrete. While hill-climbing-heuristic-based evolutionary computations are excellent at solving many optimization problems, they fail in the domains of non-continuous fitness.71 This is also the reason we do not evolve complex life or novel engineering designs.
We can conclude that 1) Simulations of evolution do not produce comparably complex artifacts. 2) Running evolutionary algorithms longer leads to progressively diminishing results.
Similar Conclusions
Others have come to similar conclusions:
It seems reasonable to assume that the number of programs possible in a given language is so inconceivably large that genetic improvement could surely not hope to find solutions in the “genetic material” of the existing program. The test input space is also, in the words of Dijkstra, “so fantastically high” that surely sampling inputs could never be sufficient to capture static truths about computation.16
Again:
[C]omputing science is — and will always be — concerned with the interplay between mechanized and human symbol manipulation, usually referred to as “computing” and “programming” respectively. An immediate benefit of this insight is that it reveals “automatic programming” as a contradiction in terms.75
More specifically:
Genetic algorithms do not scale well with complexity. That is, where the number of elements which are exposed to mutation is large there is often an exponential increase in search space size. This makes it extremely difficult to use the technique on problems such as designing an engine, a house or plane. In order to make such problems tractable to evolutionary search, they must be broken down into the simplest representation possible. Hence we typically see evolutionary algorithms encoding designs for fan blades instead of engines, building shapes instead of detailed construction plans, and airfoils instead of whole aircraft designs. The second problem of complexity is the issue of how to protect parts that have evolved to represent good solutions from further destructive mutation, particularly when their fitness assessment requires them to combine well with other parts.76
Even John Koza himself acknowledges that it would be highly surprising if his approach could work:
Anyone who has ever written and debugged a computer program and has experienced their brittle, highly non-linear, and perversely unforgiving nature will probably be understandably skeptical about the proposition that the biologically motivated process sketched above could possibly produce a useful computer program.15
My Challenge to the EA Community
I challenge the EA community to prove me wrong by producing an experiment that evolves non-trivial code from scratch and without human help. That would be the only way in which my findings could be shown to be incorrect.
On the positive side, the fact that it seems impossible to evolve complex code implies that we are unlikely to be able to directly evolve highly sophisticated artificially intelligent agents, which may present significant risk to our safety and security.77-83 Just imagine what would have happened if the very first time we ran a simulation of evolution on a computer, it produced a superintelligent agent. I have shown that Programming as a problem is AI Complete84: if GP can solve Programming that would imply that GP = AGI (Artificial General Intelligence). But we see no experimental evidence for such a claim. In fact, it is more likely that once we have AGI, it could be used to create an intelligent fitness function for GP and so make it possible to evolve code. Genetic Programming will not be the cause of Artificial Intelligence, but a product of it.
On the Other Hand, There Is Neuroevolution
Conversely, neuroevolution methods for optimizing neural networks (weights, parameters and deep learning architectures) show significant success in a number of domains and remain a strong contender for the creation of AGI.85 Karpathy suggests that neural networks represent the future of software engineering, since they allow the substitution of software based on difficult-to-write source code with automated optimization based on code evaluation:
The “classical stack” of Software 1.0 is what we’re all familiar with — it is written in languages such as Python, C++, etc. It consists of explicit instructions to the computer written by a programmer. By writing each line of code, the programmer identifies a specific point in program space with some desirable behavior. …In contrast, Software 2.0 can be written in much more abstract, human unfriendly language, such as the weights of a neural network. No human is involved in writing this code because there are a lot of weights (typical networks might have millions)… Instead, our approach is to specify some goal on the behavior of a desirable program (e.g., “satisfy a dataset of input output pairs of examples”, or “win a game of Go”), write a rough skeleton of the code (e.g. a neural net architecture), that identifies a subset of program space to search, and use the computational resources at our disposal to search this space for a program that works. In the specific case of neural networks, we restrict the search to a continuous subset of the program space where the search process can be made (somewhat surprisingly) efficient with backpropagation and stochastic gradient descent….It turns out that a large portion of real-world problems have the property that it is significantly easier to collect the data (or more generally, identify a desirable behavior) than to explicitly write the program….Software 1.0 is code we write. Software 2.0 is code written by the optimization based on an evaluation criterion (such as “classify this training data correctly”). It is likely that any setting where the program is not obvious but one can repeatedly evaluate the performance of it (e.g. — did you classify some images correctly? do you win games of Go?) will be subject to this transition, because the optimization can find much better code than what a human can write.86
Explaining Hardness
Previously, a number of attempts have been made to understand what makes certain problems hard in the context of evolutionary algorithms87, typically taking inspiration from computational complexity and subdividing all problems into GA-Easy (search space grows linearly with the size of input) and GA-Hard (search space grows exponentially with the size of input) for the problem encoded in the minimum chromosomal length.88, 89 Others, in their quest to explain Hardness, have looked at the presence in the search space of deceptive attractors which guide the search away from global maxima.90
I, likewise, suggest the existence of two broad classes of problems, based on whatever we are optimizing an existing design or trying to discover a novel design. If a particular class of problems can be represented as an optimization problem with a smooth fitness landscape, it is likely to be solvable by EAs. However, most software engineering/design problems seem to have a discontinuous fitness landscape and so are resistant to the evolutionary hill-climbing paradigm, with GP not able to outperform random search on such problems. Whether it is possible to convert such design problems into optimization problems remains an open questions and is similar in difficulty and consequences to the famous P VS NP91 problem.
While it seems unlikely that evolutionary algorithms will be able to solve design problems directly, it may be possible to use them to evolve advanced artificial intelligences (Darwin Machines92 in evolvable hardware93, 94) via neuroevolution. Such AIs in turn may be able to solve design problems. Stanley et al. also argue that neuroevolution may be a likely path to strong artificial intelligence
[B]ecause of its complexity, AGI itself is only possible to discover through an open-ended process that generates more and more complex brain-like structures indefinitely. Furthermore, open-endedness may require more than only neural networks to evolve — brains and bodies evolve together in nature, and so can morphologies evolve along with neural networks in artificial systems, providing a form of embodiment. In the long run, open-endedness could be the fuel for generating the architectures and learning algorithms that ultimately reach human-level intelligence.85
That means any safety concerns we have about advanced artificial intelligence remain as an unfortunate side-effect of research into neuroevolution. They are not eliminated due to the fundamental limits of the Darwinian algorithm.
Acknowledgments
An earlier version of this series was published as “Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms.” Evolutionary Bioinformatics. Volume 14: 1–11. 2018. © Roman V. Yampolskiy.
References


evolutionnewsandviews--2020-07-19--Copernican Revolution Promoted Man’s Place in the Cosmos

On this episode of ID the Future , host Jay Richards interviews historian of science Michael Keas about the National @ @ @ @ @ Worlds , with its claim that Copernicus ’ heliocentric model of the solar system “ @ @ @ @ @ place of honor at the center of everything .
Download the podcast or listen to @ @ @ @ @ Tyson champions this persistent myth in episode 8 of the series .
The reality is @ @ @ @ @ explains , in Copernicus ’ day , the Earth was thought to be at the @ @ @ @ @ the “ sump ” where all the filth collected , while the starry heavens were @ @ @ @ @ .
Keas and Richards trace the history of the demotion myth and discuss how Copernicus @ @ @ @ @ luminaries of the scientific revolution saw the Copernican revolution very differently , as a glorious @ @ @ @ @ place in the cosmos .

Image: Nicholas Copernicus, via Toruń Regional Museum / Public domain.
On this episode of ID the Future, host Jay Richards interviews historian of science Michael Keas about the National Geographic series Cosmos: Possible Worlds, with its claim that Copernicus’ heliocentric model of the solar system “demoted” humans from the place of honor at the center of everything. Download the podcast or listen to it here.
Neil deGrasse Tyson champions this persistent myth in episode 8 of the series. The reality is quite different. As Keas explains, in Copernicus’ day, the Earth was thought to be at the bottom of the universe, the “sump” where all the filth collected, while the starry heavens were considered the place of honor. Keas and Richards trace the history of the demotion myth and discuss how Copernicus, Kepler, and other luminaries of the scientific revolution saw the Copernican revolution very differently, as a glorious promotion of humanity’s place in the cosmos.