evolutionnewsandviews--2020-07-14--What Can and Can’t Darwin’s Algorithm Compute?

Editor ’ s note : We are delighted to welcome a new contributor , Roman V. Yampolskiy , Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering , University of Louisville .
In a new series , Dr. Yampolskiy asks : “ What Can and Can ’ t Darwin ’ s Algorithm Compute ?
” In 1859 Charles Darwin1 and many scholars before him2 , 3 have proposed theories to explain the origins of complex life forms via natural selection and modification .
Scientific theories are algorithms which , given starting conditions @ @ @ @ @ @ @ of the future state of the system .
For example , computer simulations of continental drift give us positions of continents at some time t. I have emphasized the importance of such simulations : A scientific theory can not be considered fully accepted until it can be expressed as an algorithm and simulated on a computer .
It should produce observations consistent with measurements obtained in the real world , perhaps adjusting for the relativity of time scale between simulation and the real world .
In other words , an unsimulatable hypothesis should @ @ @ @ @ @ @ one .
It is possible that the theory can not be simulated due to limits in our current computational capacity , hardware design , or capability of programmers and that it will become simulatable in the future , but until such time , it should have a tentative status.4 Simulations of Darwinian algorithm on a computer are known as Evolutionary Algorithms ( EA ) and have been around since the early days of computer science5 , 6 , with popular sub-fields such as Genetic Algorithms ( GA ) , Genetic Programming ( GP @ @ @ @ @ @ @ , and Artificial Life ( AL ) .
Currently , the state of performance in all of the above-mentioned areas is orders of magnitude less complex than what we observe in the natural world .
But why ?
A number of seminal papers have been published attempting to formalize Darwin ’ s biological theory from the point of view of computational sciences .
Such works essentially see biological evolution as a computational process running on a carbon-based substrate , but which can be run on other substrates .
Valiant in his work on @ @ @ @ @ @ @ process over mathematical functions and attempts to explain quantitatively which artifacts can be evolved with given resources , and which can not .
Likewise , Chaitin in his work on metabiology8 , 9 attempts to develop an abstract fundamental mathematical theory of evolution .
Wolfram , in A New Kind of Science10 , attempts to show how rules of a computational universe of simple programs can be used to explain some of the biological complexity we observe .
Livnat and Papadimitriou analyze sex as an algorithm , in their work on the theory @ @ @ @ @ @ @ computation11 .
• Darwin , C. , On the origin of species by means of natural selection , or .
The Preservation of Favoured Races in the Struggle for Life , London/Die Entstehung der Arten durch natürliche Zuchtwahl , Leipzig oJ , 1859 .
• Chambers , R. , Vestiges of the natural history of creation .
1853 : J. Churchill .
• Yampolskiy , R.V.
, What are the ultimate limits to computational techniques : verifier theory and unverifiability .
Physica Scripta , 2017 .
92 ( 9 ) : p. 093001 @ @ @ @ @ @ @ as evolving software , in A Computable Universe : Understanding and Exploring Nature as Computation .
2013 , World Scientific .
p. 277-302 .
• Wolfram , S. , A New Kind of Science .
May 14 , 2002 : Wolfram Media , Inc. • Livnat , A. and C. Papadimitriou , Sex as an algorithm : the theory of evolution under the lens of computation .
Communications of the ACM , 2016 .
59 ( 11 ) : p. 84-93 .

Photo: Charles Darwin in 1855, by Maull and Polyblank, Literary and Scientific Portrait Club, via Wikimedia Commons.
Editor’s note: We are delighted to welcome a new contributor, Roman V. Yampolskiy, Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. In a new series, Dr. Yampolskiy asks: “What Can and Can’t Darwin’s Algorithm Compute?”
In 1859 Charles Darwin1 and many scholars before him2, 3 have proposed theories to explain the origins of complex life forms via natural selection and modification. Scientific theories are algorithms which, given starting conditions as input, make statistically accurate predictions of the future state of the system. For example, computer simulations of continental drift give us positions of continents at some time t. I have emphasized the importance of such simulations:
A scientific theory cannot be considered fully accepted until it can be expressed as an algorithm and simulated on a computer. It should produce observations consistent with measurements obtained in the real world, perhaps adjusting for the relativity of time scale between simulation and the real world. In other words, an unsimulatable hypothesis should be considered significantly weaker than a simulatable one. It is possible that the theory cannot be simulated due to limits in our current computational capacity, hardware design, or capability of programmers and that it will become simulatable in the future, but until such time, it should have a tentative status.4
Simulations of Darwinian algorithm on a computer are known as Evolutionary Algorithms (EA) and have been around since the early days of computer science5, 6, with popular sub-fields such as Genetic Algorithms (GA), Genetic Programming (GP), Evolutionary Strategy (ES), and Artificial Life (AL). Currently, the state of performance in all of the above-mentioned areas is orders of magnitude less complex than what we observe in the natural world. But why?
Evolution as a Computational Process
A number of seminal papers have been published attempting to formalize Darwin’s biological theory from the point of view of computational sciences. Such works essentially see biological evolution as a computational process running on a carbon-based substrate, but which can be run on other substrates.
Valiant in his work on evolvability7 treats Darwinian evolution as a learning process over mathematical functions and attempts to explain quantitatively which artifacts can be evolved with given resources, and which can not. Likewise, Chaitin in his work on metabiology8, 9 attempts to develop an abstract fundamental mathematical theory of evolution. Wolfram, in A New Kind of Science10, attempts to show how rules of a computational universe of simple programs can be used to explain some of the biological complexity we observe. Livnat and Papadimitriou analyze sex as an algorithm, in their work on the theory of evolution viewed through the lens of computation11.
Tomorrow, “On Evolutionary Computation.”
References:


evolutionnewsandviews--2020-07-14--Darwinian Mythology in Strickberger’s Evolution

Editor ’ s note : Dr. Shedinger is a Professor of Religion at Luther College in Decorah , Iowa .
He is the author of a recent book critiquing Darwinian triumphalism , The Mystery of Evolutionary Mechanisms .
See also the first entry in this series , “ The Triumphalism of Strickberger ’ s Evolution.
” I have begun an extended critical review of the textbook Strickberger ’ s Evolution ( 2008 ) , edited and updated by Brian K. Hall and Benedikt Hallgrimsson .
As we might expect in any such work , Strickberger ’ s Evolution highlights the @ @ @ @ @ @ @ But the Darwin who emerges is a figure quite foreign to the Darwin who is a product of a critical historiography .
Here I will share a few of the more egregious misrepresentations of this central figure of evolutionary theory .
We begin with the textbook ’ s effusive evaluation of the Origin of Species : The only thing staggering here is the level of misrepresentation of the nature of the Origin .
Had Darwin really offered such overwhelming evidence for natural selection , he would have convinced many more of his contemporaries @ @ @ @ @ @ @ is long and distinguished ( Adam Sedgwick , Richard Owen , George Mivart , Fleeming Jenkin , to name just a few ) .
Moreover even as staunch a supporter of Darwinism as Ernst Mayr was forced to concede that “ Darwin produced embarrassingly little concrete evidence to back up some of his most important claims.
” Mayr includes evolution by natural selection as one of those claims .
In 1982 Barry Gale titled his book on Darwin , Evolution without Evidence : Charles Darwin and the Origin of Species .
And Loren @ @ @ @ @ @ @ attempt to address criticisms led later editions of the Origin to become increasingly self-contradictory and less convincing .
Any historically accurate discussion of Darwin must fully consider the arguments of his critics and the clear weaknesses of the Origin as a scientific argument .
Strickberger ’ s Evolution fails miserably on this point .
One aspect of Darwin ’ s argument emphasized in the textbook is the analogy Darwin draws between artificial and natural selection .
According to Hall and Hallgrimsson , Darwin ’ s emphasis on artificial selection firmly planted his theory @ @ @ @ @ @ @ of his contemporaries .
They thus had no choice but to accept it ( but as we saw above , many didn ’ t ! )
.
To reject Darwin ’ s theory would have required them to reject their own experiences and data .
But as we know , artificial selection is a poor analogy for natural selection since artificial selection is teleological .
Thomas Huxley ( Darwin ’ s supposed bulldog ) recognized that no evidence existed of artificial selection producing a new species , let alone natural selection .
And @ @ @ @ @ @ @ Ternate paper , wrote , “ No inferences as to variations in a state of nature can be deduced from the observation of those occurring among domestic animals.
” It sounds to me like Darwin ’ s contemporaries had no problem rejecting the analogy to artificial selection .
In terms of how Darwin came to develop his views about species change , Strickberger ’ s Evolution repeats the common hagiographical account of how Darwin was inspired by the observations he made in the Galápagos Islands : That Darwin failed to label his bird @ @ @ @ @ @ @ and even after their analysis by John Gould , Darwin chose not to include them as evidence in the Origin .
The myth about Darwin hitting on his evolutionary views while observing finches in the Galápagos resulted from the later work of ornithologist David Lack and efforts by the Ecuadorean government to inspire conservation programs in the Galápagos in the 1930s ( more on Darwin ’ s finches as an icon of evolution in the next post ) .
While rehearsing familiar aspects of the Darwinian hagiography in some parts of their textbook @ @ @ @ @ @ @ one of his key ideas in another .
In their view , “ Darwin ’ s concept of variation in relation to evolution focused on individuals rather than populations.
” But we know today , according to them , that variation in populations is what is important .
They continue : Wrong again !
Ernst Mayr is actually the one who repeatedly credits Darwin for introducing population thinking into biology and he viewed it as one of Darwin ’ s major contributions .
In his Toward a New Philosophy of Biology , Mayr @ @ @ @ @ @ @ the entirely new concept of variable populations composed of unique individuals.
” And in One Long Argument he notes that though Darwin ’ s earlier statements on species were typological , “ it is my impression that they became more populational as Darwin delved deeper into the literature of the animal breeders and also later as a result of his work on the barnacles.
” Hall and Hallgrimsson thus give credit for a key aspect of evolutionary theory to Mayr , unaware that Mayr himself had given that credit to Darwin !
Once @ @ @ @ @ @ @ evolutionary ideas is grossly distorted .
There is one point , however , where Strickberger ’ s Evolution seems to capture the importance of Darwin ’ s work , but not necessarily in the way it intends .
We are told that when Charles Darwin answered the question of what natural mechanism could explain organismal change , “ he transformed natural history ( biology as we know it today ) into a science , the science of evolution or evolutionary biology.
” In another place , we are told that the neo-Darwinian synthesis @ @ @ @ @ @ @ approach , thus eliminating Lamarckian , saltational , and orthogenetic concepts .
Truer words have never been spoken .
The importance of Darwinism isn ’ t so much in its scientific merit , but in its role of “ scientizing ” the emerging field of biology by divorcing it from its religious roots in 19th-century natural history and natural theology .
This “ scientizing ” of biology has been well chronicled by science historian Vassiliki Betty Smocovitis in Unifying Biology : the Evolutionary Synthesis and Evolutionary Biology .
Even a strong Darwinian like @ @ @ @ @ @ @ s idea is one of the most important in the history of the world because “ As an alternative explanation lacking the slightest supernatural tinge , natural selection made biology a science.
” But just because something can be counted as science does not automatically mean that it is true .
And you do not need to take it from me .
The great Darwinian paleontologist George Gaylord Simpson , while supporting methodological naturalism as a firm scientific principle , still had this to say in Tempo and Mode in Evolution : Something @ @ @ @ @ @ @ it may in fact be true .
Both Futuyma and Strickberger ’ s Evolution seem to tacitly admit that Darwin ’ s main importance is sociological , not scientific .
He helped to create a scientific field based on an understanding of science as a search for naturalistic explanation .
But his sociological importance is irrelevant to the question of the origin of species .
Charles Darwin was to be sure a fascinating and complex figure worthy of close study .
It is truly unfortunate , then , that college students using Strickberger @ @ @ @ @ @ @ sophisticated and critical engagement with Darwin they so richly deserve .
Indoctrination into Darwinian triumphalism is what they will receive .
But it is education for which they are paying tuition .
Perhaps a refund is in order .

Photo: Statue of a young Charles Darwin, Shrewsbury School, by Ailurus~frwiki / CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0).
Editor’s note: Dr. Shedinger is a Professor of Religion at Luther College in Decorah, Iowa. He is the author of a recent book critiquing Darwinian triumphalism, The Mystery of Evolutionary Mechanisms.
See also the first entry in this series, “The Triumphalism of Strickberger’s Evolution.”
I have begun an extended critical review of the textbook Strickberger’s Evolution (2008), edited and updated by Brian K. Hall and Benedikt Hallgrimsson. As we might expect in any such work, Strickberger’s Evolution highlights the life and work of Charles Darwin. But the Darwin who emerges is a figure quite foreign to the Darwin who is a product of a critical historiography. Here I will share a few of the more egregious misrepresentations of this central figure of evolutionary theory.
An Effusive Evaluation
We begin with the textbook’s effusive evaluation of the Origin of Species:
Sometimes we forget or perhaps are unaware of the scope of the evidence Darwin brought to bear on his theory of evolution by natural selection. The evidence is staggering for two reasons: its sheer weight, and the range of topics and subjects Darwin used.
The only thing staggering here is the level of misrepresentation of the nature of the Origin. Had Darwin really offered such overwhelming evidence for natural selection, he would have convinced many more of his contemporaries, but the list of his critics is long and distinguished (Adam Sedgwick, Richard Owen, George Mivart, Fleeming Jenkin, to name just a few).
Moreover even as staunch a supporter of Darwinism as Ernst Mayr was forced to concede that “Darwin produced embarrassingly little concrete evidence to back up some of his most important claims.” Mayr includes evolution by natural selection as one of those claims. In 1982 Barry Gale titled his book on Darwin, Evolution without Evidence: Charles Darwin and the Origin of Species. And Loren Eiseley famously noted how Darwin’s attempt to address criticisms led later editions of the Origin to become increasingly self-contradictory and less convincing. Any historically accurate discussion of Darwin must fully consider the arguments of his critics and the clear weaknesses of the Origin as a scientific argument. Strickberger’s Evolution fails miserably on this point.
Artificial and Natural Selection
One aspect of Darwin’s argument emphasized in the textbook is the analogy Darwin draws between artificial and natural selection. According to Hall and Hallgrimsson, Darwin’s emphasis on artificial selection firmly planted his theory in data that was familiar to many of his contemporaries. They thus had no choice but to accept it (but as we saw above, many didn’t!). To reject Darwin’s theory would have required them to reject their own experiences and data.
But as we know, artificial selection is a poor analogy for natural selection since artificial selection is teleological. Thomas Huxley (Darwin’s supposed bulldog) recognized that no evidence existed of artificial selection producing a new species, let alone natural selection. And Alfred Russel Wallace, in his famous Ternate paper, wrote, “No inferences as to variations in a state of nature can be deduced from the observation of those occurring among domestic animals.” It sounds to me like Darwin’s contemporaries had no problem rejecting the analogy to artificial selection.
In terms of how Darwin came to develop his views about species change, Strickberger’s Evolution repeats the common hagiographical account of how Darwin was inspired by the observations he made in the Galápagos Islands:
Whether his observations on these remote islands provided seeds for his later insights into the mechanisms of evolution or not, he certainly drew heavily on them for examples throughout The Origin of Species. Prominent among these was analysis of the finches he brought back from the Beagle expedition, work that was actually performed by John Gould, a prominent English ornithologist.
That Darwin failed to label his bird specimens by island is well known, and even after their analysis by John Gould, Darwin chose not to include them as evidence in the Origin. The myth about Darwin hitting on his evolutionary views while observing finches in the Galápagos resulted from the later work of ornithologist David Lack and efforts by the Ecuadorean government to inspire conservation programs in the Galápagos in the 1930s (more on Darwin’s finches as an icon of evolution in the next post).
One of Darwin’s Key Ideas
While rehearsing familiar aspects of the Darwinian hagiography in some parts of their textbook, Hall and Hallgrimsson ironically deny Darwin one of his key ideas in another. In their view, “Darwin’s concept of variation in relation to evolution focused on individuals rather than populations.” But we know today, according to them, that variation in populations is what is important. They continue:
The shift in approach to variation from individuals to populations was most clearly articulated by Ernst Mayr, who “recognized the fundamental importance of population thinking in evolutionary biology.”
Wrong again! Ernst Mayr is actually the one who repeatedly credits Darwin for introducing population thinking into biology and he viewed it as one of Darwin’s major contributions. In his Toward a New Philosophy of Biology, Mayr writes, “In 1859 Darwin introduced the entirely new concept of variable populations composed of unique individuals.” And in One Long Argument he notes that though Darwin’s earlier statements on species were typological, “it is my impression that they became more populational as Darwin delved deeper into the literature of the animal breeders and also later as a result of his work on the barnacles.” Hall and Hallgrimsson thus give credit for a key aspect of evolutionary theory to Mayr, unaware that Mayr himself had given that credit to Darwin! Once again the history of the development of evolutionary ideas is grossly distorted.
“Scientizing” Biology
There is one point, however, where Strickberger’s Evolution seems to capture the importance of Darwin’s work, but not necessarily in the way it intends. We are told that when Charles Darwin answered the question of what natural mechanism could explain organismal change, “he transformed natural history (biology as we know it today) into a science, the science of evolution or evolutionary biology.” In another place, we are told that the neo-Darwinian synthesis influenced biology because of its fundamentally materialistic approach, thus eliminating Lamarckian, saltational, and orthogenetic concepts. Truer words have never been spoken.
The importance of Darwinism isn’t so much in its scientific merit, but in its role of “scientizing” the emerging field of biology by divorcing it from its religious roots in 19th-century natural history and natural theology. This “scientizing” of biology has been well chronicled by science historian Vassiliki Betty Smocovitis in Unifying Biology: the Evolutionary Synthesis and Evolutionary Biology. Even a strong Darwinian like Douglas Futuyma has admitted that Darwin’s idea is one of the most important in the history of the world because “As an alternative explanation lacking the slightest supernatural tinge, natural selection made biology a science.”
But just because something can be counted as science does not automatically mean that it is true. And you do not need to take it from me. The great Darwinian paleontologist George Gaylord Simpson, while supporting methodological naturalism as a firm scientific principle, still had this to say in Tempo and Mode in Evolution:
As a matter of personal philosophy, I do not mean to endorse an entirely mechanistic or materialistic view of the life processes. I suspect that there is a great deal in the universe that never will be explained in such terms and much that may be inexplicable on a purely physical plane.
Something may not constitute materialistic science, but it may in fact be true.
Sociology, Not Science
Both Futuyma and Strickberger’s Evolution seem to tacitly admit that Darwin’s main importance is sociological, not scientific. He helped to create a scientific field based on an understanding of science as a search for naturalistic explanation. But his sociological importance is irrelevant to the question of the origin of species.
Charles Darwin was to be sure a fascinating and complex figure worthy of close study. It is truly unfortunate, then, that college students using Strickberger’s Evolution will be denied the sophisticated and critical engagement with Darwin they so richly deserve. Indoctrination into Darwinian triumphalism is what they will receive. But it is education for which they are paying tuition. Perhaps a refund is in order.


evolutionnewsandviews--2020-07-15--Bari Weiss Knows What ID Scientists Already Knew

New York Times opinion journalist Bari Weiss submitted her very public resignation today .
It ’ s a must read , and it will remind you at once of the world scientists in the intelligent design community have long occupied .
[ A ] new consensus has emerged in the press , but perhaps especially at this paper : that truth isn ’ t a process of collective discovery , but an orthodoxy already known to an enlightened few whose job is to inform everyone else… .
I was always taught that journalists were charged with writing the first rough @ @ @ @ @ @ @ itself is one more ephemeral thing molded to fit the needs of a predetermined narrative .
My own forays into Wrongthink have made me the subject of constant bullying by colleagues who disagree with my views .
They have called me a Nazi and a racist… .
New York Times employees publicly smear me as a liar and a bigot on Twitter with no fear that harassing me will be met with appropriate action… .
There are terms for all of this : unlawful discrimination , hostile work environment , and constructive discharge @ @ @ @ @ @ @ .
But I know that this is wrong… .
Part of me wishes I could say that my experience was unique .
But the truth is that intellectual curiosity — let alone risk-taking — is now a liability at The Times… .
[ S ] elf-censorship has become the norm .
… If a person ’ s ideology is in keeping with the new orthodoxy , they and their work remain unscrutinized .
Everyone else lives in fear of the digital thunderdome .
Online venom is excused so long as it is directed @ @ @ @ @ @ @ would have easily been published just two years ago would now get an editor or a writer in serious trouble , if not fired… .
All this bodes ill , especially for independent-minded young writers and editors paying close attention to what they ’ ll have to do to advance in their careers .
Rule One : Speak your mind at your own peril… .
I ’ ve always comforted myself with the notion that the best ideas win out .
But ideas can not win on their own .
They need a @ @ @ @ @ @ @

New York Times opinion journalist Bari Weiss submitted her very public resignation today. It’s a must read, and it will remind you at once of the world scientists in the intelligent design community have long occupied.
Some excerpts:
[A] new consensus has emerged in the press, but perhaps especially at this paper: that truth isn’t a process of collective discovery, but an orthodoxy already known to an enlightened few whose job is to inform everyone else….
I was always taught that journalists were charged with writing the first rough draft of history. Now, history itself is one more ephemeral thing molded to fit the needs of a predetermined narrative.
My own forays into Wrongthink have made me the subject of constant bullying by colleagues who disagree with my views. They have called me a Nazi and a racist….
New York Times employees publicly smear me as a liar and a bigot on Twitter with no fear that harassing me will be met with appropriate action….
There are terms for all of this: unlawful discrimination, hostile work environment, and constructive discharge. I’m no legal expert. But I know that this is wrong….
Part of me wishes I could say that my experience was unique. But the truth is that intellectual curiosity — let alone risk-taking — is now a liability at The Times…. [S]elf-censorship has become the norm.
… If a person’s ideology is in keeping with the new orthodoxy, they and their work remain unscrutinized. Everyone else lives in fear of the digital thunderdome. Online venom is excused so long as it is directed at the proper targets.
Op-eds that would have easily been published just two years ago would now get an editor or a writer in serious trouble, if not fired….
All this bodes ill, especially for independent-minded young writers and editors paying close attention to what they’ll have to do to advance in their careers. Rule One: Speak your mind at your own peril….
I’ve always comforted myself with the notion that the best ideas win out. But ideas cannot win on their own. They need a voice. They need a hearing.


evolutionnewsandviews--2020-07-15--Welcome Back, Jonathan McLatchie, and Congratulations!

It ’ s gratifying to reintroduce a longtime contributor , Jonathan McLatchie !
He is currently writing a short series @ @ @ @ @ intelligent design , so be sure to follow his writing here .
Many Evolution News @ @ @ @ @ , who has written for us for years under the name “ Jonathan M. ” @ @ @ @ @ Jonathan M. is now Dr. Jonathan McLatchie .
He earned a PhD in evolutionary biology @ @ @ @ @ Newcastle University in the UK .
Dr. McLatchie is now Assistant Professor of Biology at @ @ @ @ @ MA .
He ’ s pictured above ( r. ) in a debate , back @ @ @ @ @ University physical chemist Peter Atkins ( l. ) .
Welcome home , and congratulations !
@ @ @ @ @ McLatchie ’ s debate with Dr. Atkins is on YouTube , here ( main event @ @ @ @ @ & A ) .

Photo: Jonathan McClatchie (r.) debating Oxford University physical chemist Peter Atkins (l.) in January 2020 (screenshot).
It’s gratifying to reintroduce a longtime contributor, Jonathan McLatchie! He is currently writing a short series on Bayes’ Theorem and intelligent design, so be sure to follow his writing here. Many Evolution News readers will already know Jonathan, who has written for us for years under the name “Jonathan M.” As of January 2020, Jonathan M. is now Dr. Jonathan McLatchie. He earned a PhD in evolutionary biology from the Biosciences Institute at Newcastle University in the UK. Dr. McLatchie is now Assistant Professor of Biology at Sattler College in Boston, MA. He’s pictured above (r.) in a debate, back in January, with Oxford University physical chemist Peter Atkins (l.). Welcome home, and congratulations!
By the way, Dr. McLatchie’s debate with Dr. Atkins is on YouTube, here (main event) and here (Q&A).


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 field .
While @ @ @ @ @ @ @ 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 @ @ @ @ @ @ @ 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 @ @ @ @ @ @ @ .
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 , subject to the @ @ @ @ @ @ @ .
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 is claimed that @ @ @ @ @ @ @ 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 computing power to @ @ @ @ @ @ @ 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 software programs.
” @ @ @ @ @ @ @ 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 @ @ @ @ @ @ @ 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 like so many @ @ @ @ @ @ @ “ 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 review has revealed @ @ @ @ @ @ @ 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 with current syntax @ @ @ @ @ @ @ 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 computational power of @ @ @ @ @ @ @ 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 of some outdated @ @ @ @ @ @ @ “ [ 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 .
• Clune , J.
@ @ @ @ @ @ @ to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes .
PLoS Comput Biol 2008 .
4 ( 9 ) .
• 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 .
18 ( 5 ) : p. 779-789 .
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15 ( @ @ @ @ @ @

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--2020-07-16--The Advantages of a Bayesian Approach to ID

In two previous posts , I introduced Bayes ’ Theorem and its application to biological design .
See here : I noted that structuring the argument in the way I have proposed helps us to address some popular objections to intelligent design .
For example , consider the argument from suboptimal design , which I have addressed at Evolution News before ( e.g.
, see here and here ) .
This argument concerns the supposed unlikelihood that a designer would create biological structures that appear to be imperfect , such as the circuitous route of the recurrent laryngeal nerve .
@ @ @ @ @ @ @ a likelihood ratio of P ( E|H ) and P ( E|~H ) , this argument quickly loses its force .
Even if we concede that given examples of suboptimal design are improbable on the hypothesis of design , they are still , I would suggest , more probable on the hypothesis of design than on its falsehood .
Therefore , even granting a very low probability to the existence of apparently sub-optimally engineered systems on the hypothesis of design , such systems still count as evidence for design rather than against it @ @ @ @ @ @ @ an inference to the best explanation is that we only possess uniform and repeated experience of human agency .
We do not have experience with non-human designers , much less non-material ones ( which , in my judgment , any plausible contender for the role of designer would have to be ) .
However , as Lydia McGrew points out , “ Any attempt to use frequencies , either to make a straight inductive inference or to construct a likelihood for a Bayesian inference , must confront the problem of induction ” ( @ @ @ @ @ @ @ words , it is always a possibility that a group which has some characteristic that differs in some way from the sample already analyzed differs also with respect to the very quality about which we are interested .
McGrew gives the analogy that there is always a possibility that prehistoric civilizations did not have the ability or desire to make arrowheads .
Nevertheless , if we discover arrowheads that date to prehistoric times , it becomes irrational to reject the inference that the prehistoric arrowheads were produced by an intelligent agent simply due @ @ @ @ @ @ @ a time period different from the other makers of arrowheads with which we are familiar .
Why then , asks McGrew , should there be special hesitation about a similar use of induction when it comes to extrapolating from a known group to an unknown group in the ID debate ?
Why should the gap between human and non-human agents be of greater epistemic significance than that between human agents living in different time periods ?
It seems to be quite arbitrary .
The same principle may be applied to the objection that @ @ @ @ @ @ @ living objects , even those which are information rich and contain irreducibly complex machinery .
As McGrew again points out , reference class difficulties always occur when induction is used .
Indeed , an event or object may always be defined in such a way so as to make it unique with respect to some of its properties .
To conclude , I submit that Bayes Theorem represents a powerful way to model and structure one ’ s thinking , especially about proportioning one ’ s confidence in a conclusion relative to the @ @ @ @ @ @ @ well as informing one how one ought to update one ’ s belief in response to new evidence .
The Bayesian formulation of biological design arguments is , in my opinion , deserving of greater attention .
Bayesian inference is widely used when dealing with design in the physical sciences .
Perhaps the time has come for this structure of argument also to be used in design arguments in the life sciences .
• Barnes , L.A. ( 2018 ) “ Fine-tuning in the context of Bayesian theory testing.
” European Journal for @ @ @ @ @

Photo: Prehistoric arrowheads, by Gary Todd / CC0.
In two previous posts, I introduced Bayes’ Theorem and its application to biological design. See here:
I noted that structuring the argument in the way I have proposed helps us to address some popular objections to intelligent design.
For example, consider the argument from suboptimal design, which I have addressed at Evolution News before (e.g., see here and here). This argument concerns the supposed unlikelihood that a designer would create biological structures that appear to be imperfect, such as the circuitous route of the recurrent laryngeal nerve. However, when you consider evidence as a likelihood ratio of P(E|H) and P(E|~H), this argument quickly loses its force. Even if we concede that given examples of suboptimal design are improbable on the hypothesis of design, they are still, I would suggest, more probable on the hypothesis of design than on its falsehood. Therefore, even granting a very low probability to the existence of apparently sub-optimally engineered systems on the hypothesis of design, such systems still count as evidence for design rather than against it.
Our Experience of Human Agency
Another popular objection to design as an inference to the best explanation is that we only possess uniform and repeated experience of human agency. We do not have experience with non-human designers, much less non-material ones (which, in my judgment, any plausible contender for the role of designer would have to be). However, as Lydia McGrew points out, “Any attempt to use frequencies, either to make a straight inductive inference or to construct a likelihood for a Bayesian inference, must confront the problem of induction” (McGrew, 2004). In other words, it is always a possibility that a group which has some characteristic that differs in some way from the sample already analyzed differs also with respect to the very quality about which we are interested.
McGrew gives the analogy that there is always a possibility that prehistoric civilizations did not have the ability or desire to make arrowheads. Nevertheless, if we discover arrowheads that date to prehistoric times, it becomes irrational to reject the inference that the prehistoric arrowheads were produced by an intelligent agent simply due to the fact that he lived in a time period different from the other makers of arrowheads with which we are familiar. Why then, asks McGrew, should there be special hesitation about a similar use of induction when it comes to extrapolating from a known group to an unknown group in the ID debate? Why should the gap between human and non-human agents be of greater epistemic significance than that between human agents living in different time periods? It seems to be quite arbitrary.
The same principle may be applied to the objection that we have never observed intelligent agents design living objects, even those which are information rich and contain irreducibly complex machinery. As McGrew again points out, reference class difficulties always occur when induction is used. Indeed, an event or object may always be defined in such a way so as to make it unique with respect to some of its properties.
Belief and Evidence
To conclude, I submit that Bayes Theorem represents a powerful way to model and structure one’s thinking, especially about proportioning one’s confidence in a conclusion relative to the strength of the available evidence, as well as informing one how one ought to update one’s belief in response to new evidence.
The Bayesian formulation of biological design arguments is, in my opinion, deserving of greater attention. Bayesian inference is widely used when dealing with design in the physical sciences. Perhaps the time has come for this structure of argument also to be used in design arguments in the life sciences.
Literature Cited in This Series


On 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 also yesterday ’ s post , the first in the series , “ What Can and Can ’ t Darwin ’ s Algorithm Compute ?
“ Inspired by Darwin ’ s theory1 of biological evolution , evolutionary computation attempts to automate the process of optimization and problem solving @ @ @ @ @ @ @ individual solutions .
From the early 1950s , multiple well-documented attempts to make Darwin ’ s algorithm work on a computer have been published under such names as Evolutionary Programming12 , Evolutionary Strategies13 , Genetic Algorithms14 , Genetic Programming15 , Genetic Improvement16 , Gene Expression Programming17 , Differential Evolution18 , Neuroevolution19 , and Artificial Embryogeny20 .
While numerous variants different in their problem representation and metaheuristics exist21-24 , all can be reduced to just two main approaches — Genetic Algorithm ( GA ) and Genetic Programming ( GP ) .
GAs are used @ @ @ @ @ @ @ instance of a problem such as Shortest Total Path25 , Maximum Clique26 , Battleship27 , Sudoku28 , Mastermind23 , Light Up29 , Graph Coloring30 , integer factorization31 , 32 , or efficient halftone patterns for printers33 , and so are not the primary focus of this paper .
GPs ’ purpose , from their inception , was to automate programming by evolving an algorithm or a program for solving a particular class of problems , for example an efficient34 search algorithm .
Software design is the type of application most frequently associated with @ @ @ @ @ @ @ is also sometimes referred to as “ real programing , ” “ object-oriented GP , ” “ algorithmic programming , ” “ program synthesis , ” “ traditional programming , ” “ Turing Equivalent ( TE ) programming ” or “ Turing-complete GP ” 36-38 .
The sub-field of computation , inspired by evolution in general , and the Genetic Programing paradigm , established by John Koza in 1990s , in particular are thriving and growing exponentially .
This is evidenced both by the number of practitioners and of scientific publications .
Petke @ @ @ @ @ @ @ of number of publications with the Genetic Programming Bibliography passing 10,000 entries … By 2016 there were nineteen GP books including several intended for students … ” 16 .
Such tremendous growth has been fueled , since the early days , by belief in the capabilities of evolutionary algorithms , and our ability to overcome obstacles of limited computational power or data as illustrated by the following comments : • “ We will ( before long ) be able to run genetic algorithms on computers that are sufficiently fast to recreate on a @ @ @ @ @ @ @ optimization power that the relevant processes of natural selection instantiated throughout our evolutionary past … ” 39 • “ As computational devices improve in speed , larger problem spaces can be searched.
” 40 • “ Evolution is a slow learner , but the steady increase in computing power , and the fact that the algorithm is inherently suited to parallelization , mean that more and more generations can be executed within practically acceptable timescales.
” 41 • “ We believe that in about fifty years ’ time it will be possible to @ @ @ @ @ @ @ Not merely possible but indeed prevalent.
” 42 • “ The relentless iteration of Moore ’ s law promises increased availability of computational resources in future years .
If available computer capacity continues to double approximately every 18 months over the next decade or so , a computation requiring 80 h will require only about 1 % as much computer time ( i.e.
, about 48 min ) a decade from now .
That same computation will require only about 0.01 % as much computer time ( i.e.
, about 48 seconds ) @ @ @ @ @ @ @ forward , we believe that genetic programming can be expected to be increasingly used to automatically generate ever-more complex human-competitive results.
” 43 • “ The production of human-competitive results as well as the increased intricacy of the results are broadly correlated to increased availability of computing power tracked by Moore ’ s law .
The production of human-competitive results using genetic programming has been greatly facilitated by the fact that genetic algorithms and other methods of evolutionary computation can be readily and efficiently parallelized .
… Additionally , the production of human-competitive @ @ @ @ @ @ @ an even greater degree by the increased availability of computing power , over a period of time , as tracked by Moore ’ s law .
Indeed , over the past two decades , the number and level of intricacy of the human-competitive results has progressively grown .
… [ T ] here is , nonetheless , data indicating that the production of human-competitive results using genetic programming is broadly correlated with the increased availability of computer power , from year to year , as tracked by Moore ’ s Law.
” 43 @ @ @ @ @ @ @ data generation techniques , an abundance of source code publicly available , and importance of nonfunctional properties have combined to create a technical and scientific environment ripe for the exploitation of genetic improvement.
” 40 • Mayr , E. , Behavior Programs and Evolutionary Strategies : Natural selection sometimes favors a genetically ” closed ” behavior program , sometimes an ” open ” one .
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, Genetic improvement of software : a comprehensive survey .
IEEE Transactions on Evolutionary Computation , 2017 .
• Ferreira , C. , Gene expression programming : mathematical modeling by an artificial intelligence .
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, L. Ashby , and L. Hassan , Wisdom of Artificial Crowds—A Metaheuristic Algorithm for Optimization .
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International Journal of Bio-inspired computation , 2011 .
3 @ @ @ @ @ @ @ • Khalifa , A.B .
and R.V .
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Yampolskiy .
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and R.V .
Yampolskiy , Shortest Total Path Length Spanning Tree via @ @ @ @ @ @ @ The 28th Modern Artificial Intelligence and Cognitive Science Conference ( MAICS2017 ) .
April 28-29 , 2017 : Fort Wayne , IN , USA .
• Ouch , R. , K. Reese , and R.V .
Yampolskiy .
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Using a GA and Wisdom of Artificial Crowds to solve solitaire battleship puzzles .
in Computer Games ( CGAMES ) , 2012 17th International Conference on .
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Yampolskiy , Solving Sudoku Puzzles with Wisdom of Artificial Crowds .
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• Ashby , L.H .
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• Mishra , M. , S. Pal , and R. Yampolskiy , Nature-Inspired Computing Techniques for Integer Factorization .
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p. 1-16 .

Photo credit: Photo by John Karlo Mendoza on 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 also yesterday’s post, the first in the series, “What Can and Can’t Darwin’s Algorithm Compute?“
Inspired by Darwin’s theory1 of biological evolution, evolutionary computation attempts to automate the process of optimization and problem solving by simulating differential survival and reproduction of individual solutions. From the early 1950s, multiple well-documented attempts to make Darwin’s algorithm work on a computer have been published under such names as Evolutionary Programming12, Evolutionary Strategies13, Genetic Algorithms14, Genetic Programming15, Genetic Improvement16, Gene Expression Programming17, Differential Evolution18, Neuroevolution19, and Artificial Embryogeny20. While numerous variants different in their problem representation and metaheuristics exist21-24, all can be reduced to just two main approaches — Genetic Algorithm (GA) and Genetic Programming (GP).
GAs are used to evolve optimized solutions to a particular instance of a problem such as Shortest Total Path25, Maximum Clique26, Battleship27, Sudoku28, Mastermind23, Light Up29, Graph Coloring30, integer factorization31, 32, or efficient halftone patterns for printers33, and so are not the primary focus of this paper. GPs’ purpose, from their inception, was to automate programming by evolving an algorithm or a program for solving a particular class of problems, for example an efficient34 search algorithm. Software design is the type of application most frequently associated with GPs35, but work in automated programming is also sometimes referred to as “real programing,” “object-oriented GP,” “algorithmic programming,” “program synthesis,” “traditional programming,” “Turing Equivalent (TE) programming” or “Turing-complete GP”36-38.
Tremendous Growth
The sub-field of computation, inspired by evolution in general, and the Genetic Programing paradigm, established by John Koza in 1990s, in particular are thriving and growing exponentially. This is evidenced both by the number of practitioners and of scientific publications. Petke et al. observe “…enormous expansion of number of publications with the Genetic Programming Bibliography passing 10,000 entries … By 2016 there were nineteen GP books including several intended for students …”16. Such tremendous growth has been fueled, since the early days, by belief in the capabilities of evolutionary algorithms, and our ability to overcome obstacles of limited computational power or data as illustrated by the following comments:
“We will (before long) be able to run genetic algorithms on computers that are sufficiently fast to recreate on a human timescale the same amount of cumulative optimization power that the relevant processes of natural selection instantiated throughout our evolutionary past … ” 39
“As computational devices improve in speed, larger problem spaces can be searched.” 40
“Evolution is a slow learner, but the steady increase in computing power, and the fact that the algorithm is inherently suited to parallelization, mean that more and more generations can be executed within practically acceptable timescales.” 41
“We believe that in about fifty years’ time it will be possible to program computers by means of evolution. Not merely possible but indeed prevalent.” 42
“The relentless iteration of Moore’s law promises increased availability of computational resources in future years. If available computer capacity continues to double approximately every 18 months over the next decade or so, a computation requiring 80 h will require only about 1% as much computer time (i.e., about 48 min) a decade from now. That same computation will require only about 0.01% as much computer time (i.e., about 48 seconds) in two decades. Thus, looking forward, we believe that genetic programming can be expected to be increasingly used to automatically generate ever-more complex human-competitive results.” 43
“The production of human-competitive results as well as the increased intricacy of the results are broadly correlated to increased availability of computing power tracked by Moore’s law. The production of human-competitive results using genetic programming has been greatly facilitated by the fact that genetic algorithms and other methods of evolutionary computation can be readily and efficiently parallelized. … Additionally, the production of human-competitive results using genetic programming has facilitated to an even greater degree by the increased availability of computing power, over a period of time, as tracked by Moore’s law. Indeed, over the past two decades, the number and level of intricacy of the human-competitive results has progressively grown. … [T]here is, nonetheless, data indicating that the production of human-competitive results using genetic programming is broadly correlated with the increased availability of computer power, from year to year, as tracked by Moore’s Law.” 43
“[P]owerful test data generation techniques, an abundance of source code publicly available, and importance of nonfunctional properties have combined to create a technical and scientific environment ripe for the exploitation of genetic improvement.”40
Tomorrow, “State-of-the-Art in Evolutionary Computation.”
References:


evolutionnewsandviews--2020-07-15--“Live Not by Lies”: Aleksandr Solzhenitsyn and Intelligent Design

I got involved in the intelligent design movement before I became a Christian , although the insight that ID offered became important in my conversion .
I had been an atheist , or at least an agnostic .
I took a Randian ( as in Ayn Rand ) perspective on religion : religion was just another form of rent-seeking for priests , a way of making a living by plying a lie .
I thought God didn ’ t exist , not because I had worked out the arguments and came to that conclusion ( like most atheists I understood @ @ @ @ @ @ @ , but because , as Laplace reportedly said to Napoleon about God , “ I have no need of that hypothesis.
” Life was in need of explanation , and Darwin provided it .
Man , and all living things , evolved by variation and selection , from some primordial accident of chemicals .
Science had proven this , I thought , so why waste my time in a church pew ?
Two things drew me into ID .
First , I was aghast at the treatment of ID theorists and other skeptics @ @ @ @ @ @ @ , but goodness gracious Darwinists were nasty .
It didn ’ t seem to me like good science : my beloved mentor , biologist Bob Pollack with whom I did research in college , said : “ A good scientist is always his own most exacting critic.
” Darwinists spent their time excoriating IDers , and seemed to care little for genuine critiques of their own science .
I thought : if Darwinism is good science ( as I thought it was ) , Darwinists should welcome public debate and engage honest discussion @ @ @ @ @ @ @ the facts , what is there to fear ?
But instead , they Expelled anyone who questioned Darwinism .
They insulted people , intimidated them , used the courts to silence them , and callously ruined their careers .
This was Lysenkoism , not science .
Second , I had the good fortune to read ID pioneers : Phillip Johnson , Michael Denton , Richard Sternberg , Jonathan Wells , Michael Behe , William Dembski , among others .
They made sense .
A lot of sense .
So , over time , @ @ @ @ @ @ @ ID in my own research , applying engineering principles to understand blood flow in the brain .
It is good science , honest science .
Real science .
In my own career , my embrace of ID has had little negative effect .
I got promoted and got tenure and I advanced in my career without Darwinist hinderance .
They tried — there have been many calls to my university demanding that I be fired , and I ’ ve had death threats .
But I have done fine , due to the @ @ @ @ @ @ @ and the fact that I have a day job that doesn ’ t depend on Darwinists ’ opinions of me .
Not so for many of my colleagues in the ID community , who depend on grants and on academic decisions made by Darwinists , and who have suffered greatly .
The ID folks who have sacrificed careers and withstood public excoriation and even judicial attack to tell the truth about design in nature are heroes , plain and simple .
I ’ ve watched current events , especially “ cancel culture , @ @ @ @ @ @ @ sense of familiarity .
The film Expelled is , after all , a documentary about professional “ cancellation , ” a decade or so before cancellation in America went viral .
It is as if the Darwinist assault on ID was a run-up to the contemporary massive assault on intellectual freedom .
Dissenters are denigrated , slandered , excluded , and fired for expressing opinions that just a few years ago were widely expressed and often still represent majority opinion .
The Darwinist trope that criticism of Darwinist theory is “ anti-science ” @ @ @ @ @ @ @ ’ s trope that free speech is a kind of “ violence.
” It ’ s the same segue — the Darwinist/cancel culture mob ’ s response to reasoned discourse is “ Nice career you ’ ve got there — shame if something happened to it.
” There is , obviously , a totalitarian flavor to all of this , and ( remarkably ) it is imposed on us by us .
We ’ re doing this to ourselves .
No Bolshevik faction has stormed Washington and occupied the White House and commandeered @ @ @ @ @ @ @ expel/cancel ourselves .
So how do those of us who cherish scientific integrity and the human rights endowed by the Lord and enumerated in our Constitution stand up for academic freedom and freedom of speech in these times ?
Recently , on reading Aleksandr Solzhenitsyn ’ s short essay “ Live Not by Lies , ” I was struck by the analogy between the totalitarian system Solzhenitsyn faced , the Lysenkoism of the Darwinist suppression of ID , and the “ woke ” assault on freedom of speech and freedom of thought spreading @ @ @ @ @ @ @ writes : The parallels are obvious .
If you doubted the Soviet system , or doubt Darwinism , or if you doubt any of the tropes currently on tap in the woke-sphere , you do well to keep silent , if you value your reputation and your career , and , increasingly , if you value your personal safety .
The Wrong Side of the Mob And so we fear to speak out for scientific integrity or freedom of speech lest we find ourselves on the wrong side of the mob or as @ @ @ @ @ @ @ .
Survival in the scientific profession , and ( increasingly ) survival in the social , economic , and political realm depends on accepting a very specific kind of dogma .
Solhenitsyn : If we take a knee — if we pay homage to Darwinian stories or accede to the mob — we can get on with our lives , even prosper .
But at the price of our minds and our souls .
Solzhenitsyn explains the essential role that our lies play in enforcing conformity : The use of force to enforce @ @ @ @ @ @ @ be sustained .
Darwinists can ’ t spend all their time destroying colleagues ’ careers .
Mobs can ’ t fill the streets day and night .
Cancellers depend on your complicity .
They depend on you to lie .
With this insight — the fundamental insight by which we can fight cancel culture in the lab and the classroom and in the world — Solzhenitsyn sees how each of us can fight the censors .
Darwinist ( and other ) censors depend on our personal participation in lies to silence us , @ @ @ @ @ @ @ they can not counter : personal non-participation in lies , a kind of civil disobedience .
Solzhenitsyn recommends a series of acts of non-participation : we refuse to write or endorse or speak any statement in science or ethics or politics that is untrue .
We refuse to be compelled to attend meetings or activities if they are contrary to truth .
We will walk out of meetings in which lies are promulgated .
We will only read and endorse literature that advances truth .
Solzhenitsyn has no illusions about the difficulty and @ @ @ @ @ @ @ Even this non-participation in lies will be very difficult , but it is a powerful kind of resistance .
If you can , fight scientific censorship and cancel culture actively — by speaking out , by writing , by activism .
Do ID research .
Support Discovery Institute .
But if you are in a position in which you can not actively resist — if because of your family or your circumstances you can not risk your livelihood — you can quietly resolve not to participate in lies .
The censors depend on @ @ @ @ @ @ @ , but to get us ( out of fear ) to lie and thus silence ourselves .
Their power is not in their violence but in our complicity .
Don ’ t be complicit .
Don ’ t participate .
Don ’ t give assent to Darwinist or atheist or Marxist lies .
If you ’ re a teacher , let your students know ( implicitly if you can not do so explicitly ) that the Darwinism you are forced to teach is not , as the censors insist , a perfect doctrine @ @ @ @ @ @ @ If you are a scientist , don ’ t include nonsensical Darwinist tropes in your publications or even in your conversations .
When others tell Darwinian fairytales , if you are not in a position to openly reply , a quip or a joke ( they ’ re terrified of ridicule ) or just a smirk or rolled eyes is resistance .
You can always just walk away .
Censors of all sorts depend on the cooperation of their victims .
Don ’ t cooperate .
Don ’ t participate .
Serve only @ @ @ @ @ @ @ .

Photo: Aleksandr Solzhenitsyn, by Verhoeff, Bert / Anefo / CC0.
I got involved in the intelligent design movement before I became a Christian, although the insight that ID offered became important in my conversion. I had been an atheist, or at least an agnostic. I took a Randian (as in Ayn Rand) perspective on religion: religion was just another form of rent-seeking for priests, a way of making a living by plying a lie.
I thought God didn’t exist, not because I had worked out the arguments and came to that conclusion (like most atheists I understood virtually nothing about the real arguments), but because, as Laplace reportedly said to Napoleon about God, “I have no need of that hypothesis.” Life was in need of explanation, and Darwin provided it. Man, and all living things, evolved by variation and selection, from some primordial accident of chemicals. Science had proven this, I thought, so why waste my time in a church pew?
How Good Science Behaves?
Two things drew me into ID. First, I was aghast at the treatment of ID theorists and other skeptics by Darwinists. I believed in Darwinism, but goodness gracious Darwinists were nasty. It didn’t seem to me like good science: my beloved mentor, biologist Bob Pollack with whom I did research in college, said: “A good scientist is always his own most exacting critic.” Darwinists spent their time excoriating IDers, and seemed to care little for genuine critiques of their own science. I thought: if Darwinism is good science (as I thought it was), Darwinists should welcome public debate and engage honest discussion. After all, if they had the facts, what is there to fear? But instead, they Expelled anyone who questioned Darwinism. They insulted people, intimidated them, used the courts to silence them, and callously ruined their careers. This was Lysenkoism, not science.
Second, I had the good fortune to read ID pioneers: Phillip Johnson, Michael Denton, Richard Sternberg, Jonathan Wells, Michael Behe, William Dembski, among others. They made sense. A lot of sense. So, over time, I embraced ID. I even used ID in my own research, applying engineering principles to understand blood flow in the brain. It is good science, honest science. Real science.
In my own career, my embrace of ID has had little negative effect. I got promoted and got tenure and I advanced in my career without Darwinist hinderance. They tried — there have been many calls to my university demanding that I be fired, and I’ve had death threats. But I have done fine, due to the integrity and good nature of my bosses and the fact that I have a day job that doesn’t depend on Darwinists’ opinions of me. Not so for many of my colleagues in the ID community, who depend on grants and on academic decisions made by Darwinists, and who have suffered greatly. The ID folks who have sacrificed careers and withstood public excoriation and even judicial attack to tell the truth about design in nature are heroes, plain and simple.
Cancel Culture
I’ve watched current events, especially “cancel culture,” with interest and with an unsettling sense of familiarity. The film Expelled is, after all, a documentary about professional “cancellation,” a decade or so before cancellation in America went viral. It is as if the Darwinist assault on ID was a run-up to the contemporary massive assault on intellectual freedom. Dissenters are denigrated, slandered, excluded, and fired for expressing opinions that just a few years ago were widely expressed and often still represent majority opinion. The Darwinist trope that criticism of Darwinist theory is “anti-science” is the prologue to the woke mob’s trope that free speech is a kind of “violence.” It’s the same segue — the Darwinist/cancel culture mob’s response to reasoned discourse is “Nice career you’ve got there — shame if something happened to it.”
There is, obviously, a totalitarian flavor to all of this, and (remarkably) it is imposed on us by us. We’re doing this to ourselves. No Bolshevik faction has stormed Washington and occupied the White House and commandeered the army. We have chosen to expel/cancel ourselves.
So how do those of us who cherish scientific integrity and the human rights endowed by the Lord and enumerated in our Constitution stand up for academic freedom and freedom of speech in these times?
“Live Not by Lies”
Recently, on reading Aleksandr Solzhenitsyn’s short essay “Live Not by Lies,” I was struck by the analogy between the totalitarian system Solzhenitsyn faced, the Lysenkoism of the Darwinist suppression of ID, and the “woke” assault on freedom of speech and freedom of thought spreading like fire though our nation. Solzhenitsyn writes:
At one time we dared not even to whisper. Now we write and read samizdat, and sometimes when we gather… we complain frankly to one another: What kind of tricks are they playing on us, and where are they dragging us? Is there any way out? And they put on trial anybody they want and they put sane people in asylums — always they, and we are powerless… what can we do to stop it?
The parallels are obvious. If you doubted the Soviet system, or doubt Darwinism, or if you doubt any of the tropes currently on tap in the woke-sphere, you do well to keep silent, if you value your reputation and your career, and, increasingly, if you value your personal safety.
… for today’s modest ration of food we are willing to abandon all our principles, our souls, and all the efforts of our predecessors and all opportunities for our descendants — but just don’t disturb our fragile existence. We lack staunchness, pride and enthusiasm… We have already taken refuge in the crevices. We just fear acts of civil courage. We fear only to lag behind the herd and to take a step alone — and suddenly find ourselves without white bread, without heating gas and without a Moscow registration.
The Wrong Side of the Mob
And so we fear to speak out for scientific integrity or freedom of speech lest we find ourselves on the wrong side of the mob or as pariahs without a career or a business.
Survival in the scientific profession, and (increasingly) survival in the social, economic, and political realm depends on accepting a very specific kind of dogma. Solhenitsyn:
We have been indoctrinated in political courses, and in just the same way was fostered the idea to live comfortably, and all will be well for the rest of our lives…
If we take a knee — if we pay homage to Darwinian stories or accede to the mob — we can get on with our lives, even prosper. But at the price of our minds and our souls.
The circle — is it closed? And is there really no way out? And is there only one thing left for us to do, to wait without taking action? Maybe something will happen by itself? It will never happen as long as we daily acknowledge, extol, and strengthen — and do not sever ourselves from the most perceptible of its aspects: Lies.
Our Lives, Our Lies
Solzhenitsyn explains the essential role that our lies play in enforcing conformity:
When violence intrudes into peaceful life, its face glows with self-confidence, as if it were carrying a banner and shouting: “I am violence. Run away, make way for me — I will crush you.” But violence quickly grows old. And it has lost confidence in itself, and in order to maintain a respectable face it summons falsehood as its ally — since violence lays its ponderous paw not every day and not on every shoulder. It demands from us only obedience to lies and daily participation in lies — all loyalty lies in that.
The use of force to enforce conformity is limited: it cannot be sustained. Darwinists can’t spend all their time destroying colleagues’ careers. Mobs can’t fill the streets day and night. Cancellers depend on your complicity. They depend on you to lie. With this insight — the fundamental insight by which we can fight cancel culture in the lab and the classroom and in the world — Solzhenitsyn sees how each of us can fight the censors.
And the simplest and most accessible key to our self-neglected liberation lies right here: Personal non-participation in lies. Though lies conceal everything, though lies embrace everything, but not with any help from me. This opens a breach in the imaginary encirclement caused by our inaction. It is the easiest thing to do for us, but the most devastating for the lies. Because when people renounce lies it simply cuts short their existence. Like an infection, they can exist only in a living organism.
Our Personal Participation
Darwinist (and other) censors depend on our personal participation in lies to silence us, and this provides us with a weapon they cannot counter: personal non-participation in lies, a kind of civil disobedience.
[L]et us refuse to say that which we do not think… we [will] be astonished how quickly the lies would be rendered helpless and subside.
Solzhenitsyn recommends a series of acts of non-participation: we refuse to write or endorse or speak any statement in science or ethics or politics that is untrue. We refuse to be compelled to attend meetings or activities if they are contrary to truth. We will walk out of meetings in which lies are promulgated. We will only read and endorse literature that advances truth.
Solzhenitsyn has no illusions about the difficulty and potential consequences of non-participation in lies.
Some, at first, will lose their jobs. For young people who want to live with truth, this will, in the beginning, complicate their young lives very much, because the required recitations are stuffed with lies, and it is necessary to make a choice. But there are no loopholes for anybody who wants to be honest. On any given day any one of us will be confronted with at least one of the above-mentioned choices even in the most secure of the technical sciences. Either truth or falsehood: Toward spiritual independence or toward spiritual servitude… It will not be an easy choice for a body, but it is the only one for a soul.
Even this non-participation in lies will be very difficult, but it is a powerful kind of resistance.
So you will not be the first to take this path, but will join those who have already taken it. This path will be easier and shorter for all of us if we take it by mutual efforts and in close rank. If there are thousands of us, they will not be able to do anything with us. If there are tens of thousands of us, then we would not even recognize our country.
If you can, fight scientific censorship and cancel culture actively — by speaking out, by writing, by activism. Do ID research. Support Discovery Institute. But if you are in a position in which you cannot actively resist — if because of your family or your circumstances you cannot risk your livelihood — you can quietly resolve not to participate in lies.
They’re Terrified of Ridicule
The censors depend on active personal destruction not to silence us, but to get us (out of fear) to lie and thus silence ourselves. Their power is not in their violence but in our complicity.
Don’t be complicit. Don’t participate. Don’t give assent to Darwinist or atheist or Marxist lies. If you’re a teacher, let your students know (implicitly if you cannot do so explicitly) that the Darwinism you are forced to teach is not, as the censors insist, a perfect doctrine with strengths only and no weaknesses. If you are a scientist, don’t include nonsensical Darwinist tropes in your publications or even in your conversations. When others tell Darwinian fairytales, if you are not in a position to openly reply, a quip or a joke (they’re terrified of ridicule) or just a smirk or rolled eyes is resistance. You can always just walk away.
Censors of all sorts depend on the cooperation of their victims. Don’t cooperate. Don’t participate. Serve only the truth. Live not by lies.