Darwinists: did we say a tree? What we meant was..

 Evolutionists Walk it Back Again: Human Evolution is More a Muddy Delta Than a Branching Tree

Cornelius G Hunter

First it was a tree, then it was a bush, then it was a network, and now it is a muddy delta. The evolutionary model of how the species are supposed to be related has failed over and over. And John Hawks’ latest version of this moving target reveals yet again that the theory of evolution is not explanatory, and that the evidence contradicts the theory. Hawks explains that the latest thinking on how the primates evolved “is no evolutionary tree. Our evolutionary history is like a braided stream.”

Evolution is a blind guide—it is always wrong. It is always pointing in the wrong direction, and evolutionists are always having to walk it back and do their damage control.

When will they learn?

The case for conditionalism

 

On design and "evolution"

 Peer-Reviewed Paper by Discovery Institute Staff Evaluates Synthesis of “Design and Evolution” 

Casey Luskin

We are pleased to announce the publication of a peer-reviewed open-access article by CSC staff members Stephen Dilley, Brian Miller, Emily Reeves, and myself. Published in the journal Religions, our article is titled “On the Relationship Between Design and Evolution,” and it examines and reviews a scholarly book, The Compatibility of Evolution and Design (Palgrave Macmillan / 2021), which is arguably the best current treatment of the relationship between evolution and intelligent design from an evolutionary point of view. The book is written by E. V. Rope Kojonen, a theologian at the University of Helsinki, who offers a potent argument that mainstream evolutionary biology is fully compatible with a robust biological design argument. On his view, the wings of the hummingbird, for example, display evidence of design while also being the product of natural selection, random mutation, and other processes — all without the need for direct guidance, divine intervention, or intelligent supervision per se.  

We regard Kojonen’s model as nuanced, erudite, and fair-minded. It is a model of fine scholarship and deserves serious attention. Even so, we argue that Kojonen’s conception of design is flawed, as is his attempt to harmonize design with evolution. We support our contentions with both scientific and philosophical arguments. Scientifically, we provide perhaps the most comprehensive defense of Douglas Axe’s research written to date as well as an updated analysis of the bacterial flagellum. Philosophically, we argue that Kojonen’s model undercuts itself. It gives an account of “design detection” that actually conflicts with Kojonen’s own design argument.

A Question about Compatibility

The abstract of the article is as follows:

 A longstanding question in science and religion is whether standard evolutionary models are compatible with the claim that the world was designed. In The Compatibility of Evolution and Design, theologian E. V. Rope Kojonen constructs a powerful argument that not only are evolution and design compatible, but that evolutionary processes (and biological data) strongly point to design. Yet Kojonen’s model faces several difficulties, each of which raise hurdles for his understanding of how evolution and design can be harmonized. First, his argument for design (and its compatibility with evolution) relies upon a particular view of nature in which fitness landscapes are “fine-tuned” to allow proteins to evolve from one form to another by mutation and selection. But biological data run contrary to this claim, which poses a problem for Kojonen’s design argument (and, as such, his attempt to harmonize design with evolution). Second, Kojonen appeals to the bacterial flagellum to strengthen his case for design, yet the type of design in the flagellum is incompatible with mainstream evolutionary theory, which (again) damages his reconciliation of design with evolution. Third, Kojonen regards convergent evolution as notable positive evidence in favor of his model (including his version of design), yet convergent evolution actually harms the justification of common ancestry, which Kojonen also accepts. This, too, mars his reconciliation of design and evolution. Finally, Kojonen’s model damages the epistemology that undergirds his own design argument as well as the design intuitions of everyday “theists on the street”, whom he seeks to defend. Thus, despite the remarkable depth, nuance, and erudition of Kojonen’s account, it does not offer a convincing reconciliation of “design” and “evolution”.

A Flawed Account of Design

We’ll have more to say here about this model in the coming weeks, but for now, it’s worth taking a look at some of the main points from our paper’s conclusion:

In this article, we argued that Kojonen’s account of design is flawed. It requires fine-tuned preconditions (and smooth fitness landscapes) so that evolution can successfully search and build viable biological forms. Yet empirical evidence shows that no such preconditions or fitness landscapes exist. At precisely the place we would expect to find evidence of Kojonen’s type of “design”, we find no such thing. Accordingly, his view of design is at odds with the evidence itself. As such, it is poorly situated to add explanatory value to evolution.

We also contended that Kojonen’s conjunction of “design” and “evolution” is internally fragmented. Recall that Kojonen believes that the complexity of the bacterial flagellum adds to his case for joining “design” to “evolution”. Yet Behe’s irreducible complexity argument shows that the type of design manifest in the bacterial flagellum runs contrary to mainstream evolution. Thus, the very system that provides strong evidence of design also undercuts evolution. In effect, this drives a wedge between the two. Kojonen’s conjunction of “design and evolution” is at war with itself.

We also highlighted the internal tension in Kojonen’s attempt to join “design” and “evolution” with respect to convergent evolution. Kojonen draws on convergence as a key argument for the “laws of form”, which are an important element of fine-tuned preconditions and, thus, his case for design. Yet convergent evolution conflicts with Kojonen’s use of co-option and approach to protein evolution. It also conflicts with the general justification of common ancestry. Thus, this element of Kojonen’s case for design chafes against his own reasoning as well as mainstream evolutionary thought. Internal discord surfaces once again.

In each of these criticisms, we have not targeted evolutionary theory itself. Although we believe that the scientific evidence we have covered counters mainstream evolution, we have set this concern aside in this article. Instead, our criticisms are aimed at Kojonen’s conception of design. We have contended that he does not offer sufficient empirical support for it — and so it adds little explanatory merit to “evolution” — and that some of the evidence he does offer actually conflicts with his commitment to evolution, producing incoherence within his model. (We should note, however, that because of the way Kojonen frames the matter, our criticisms of his view of design do have negative implications for the feasibility of evolutionary theory as he understands it. But this is an implication of our argument based on his own framing. It is not the focus of our argument per se. We will return to this point momentarily.)

Finally, we raised epistemological concerns aimed at the fundamental basis of Kojonen’s understanding of design detection. If our concerns are correct, then they cut deeply against Kojonen’s design argument as well as his defense of the theist on the street. In a nutshell, our worry is that a person who takes Kojonen’s model seriously — or who lived in such a universe — would either have defeaters for her biology-based design beliefs or might not have the cognitive dispositions and beliefs that (in our experience) are foundational to the formation of such beliefs in the first place. Kojonen’s reliance on evolution (and non-agent causes) undermines his basis for design detection, in short.

Stepping back, it is important to reiterate, once again, the many strengths of Kojonen’s treatment. The extensive review we have given here is a credit to a book of remarkable sophistication, precision, and erudition. Only a venerable fortress is worthy of a long siege. The Compatibility of Evolution and Design is the best of its class.

Devastating Implications for Evolution

But there’s one more point worth highlighting about Kojonen’s model. He effectively concedes that evolution won’t work to produce biological complexity unless there is some special “fine-tuning” of the “preconditions” for evolution (which themselves arise from designed laws of nature). We might agree with this framing but we have shown that this fine-tuning does not seem to exist. Therefore not only is his case for marrying design and evolution flawed but — if there are no such preconditions — then evolution itself is impotent. Here’s how we frame this in the final paragraph:

Even so, we bring this article to a close on a poignant note: Kojonen’s model may have devastating implications for mainstream evolutionary theory. Recall that the heart of his proposal is that evolution needs design (in the form of fine-tuned preconditions). Evolution on its own is insufficient to produce flora and fauna. But if we are correct that Kojonen’s conception and justification of design are flawed, then it follows — by his own lights — that evolution is impotent to explain biological complexity. Kojonen’s own account of the efficacy of evolution depends upon the success of his case for design. But if the latter stumbles, then so does the former. In a startling way, Kojonen has set the table for the rejection of evolution. If he has failed to make his case for design, then he has left readers with strong reasons to abandon mainstream evolutionary theory. The full implications of this striking result warrant further exploration.

Despite our critique of Kojonen’s model, we find it stimulating and thoughtful. We invite interested readers of Evolution News to read our article and also to read The Compatibility of Evolution and Design.

Yet more primeval tech vs. Darwinism.

 

On the edge of Darwinism

 

Natural selection as conserved.

 

Photosynthesis vs.Darwinism

 

Paved with good intentions? II

 

It's official: Less is more

 

To brash for Mikhail tal?

 

Darwinism does not compute?

 On Evolutionary Computation

 

 Roman V. Yampolskiy

 

 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:

Back, T., Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. 1996: Oxford university press.

Mayr, E., Behavior Programs and Evolutionary Strategies: Natural selection sometimes favors a genetically” closed” behavior program, sometimes an” open” one. American scientist, 1974. 62(6): p. 650-659.

Davis, L., Handbook of genetic algorithms. 1991: Van Nostrand Reinhold.

Koza, J.R., Genetic programming as a means for programming computers by natural selection. Statistics and computing, 1994. 4(2): p. 87-112.

Petke, J., et al., Genetic improvement of software: a comprehensive survey. IEEE Transactions on Evolutionary Computation, 2017.

Ferreira, C., Gene expression programming: mathematical modeling by an artificial intelligence. Vol. 21. 2006: Springer.

Storn, R. and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997. 11(4): p. 341-359.

Such, F.P., et al., Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. arXiv preprint arXiv:1712.06567, 2017.

Stanley, K.O. and R. Miikkulainen, A taxonomy for artificial embryogeny. Artificial Life, 2003. 9(2): p. 93-130.

Yampolskiy, R.V., L. Ashby, and L. Hassan, Wisdom of Artificial Crowds—A Metaheuristic Algorithm for Optimization. Journal of Intelligent Learning Systems and Applications, 2012. 4(2): p. 98-107.

Yampolskiy, R.V. and A. El-Barkouky, Wisdom of artificial crowds algorithm for solving NP-hard problems. International Journal of Bio-inspired computation, 2011. 3(6): p. 358-369.

Khalifa, A.B. and R.V. Yampolskiy, GA with Wisdom of Artificial Crowds for Solving Mastermind Satisfiability Problem. Int. J. Intell. Games & Simulation, 2011. 6(2): p. 12-17.

Lowrance, C.J., O. Abdelwahab, and R.V. Yampolskiy. Evolution of a Metaheuristic for Aggregating Wisdom from Artificial Crowds. in Portuguese Conference on Artificial Intelligence. 2015. Springer.

Hundley, M.V. and R.V. Yampolskiy, Shortest Total Path Length Spanning Tree via Wisdom of Artificial Crowds Algorithm, in 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. Hybrid Genetic Algorithm for the Maximum Clique Problem Combining Sharing and Migration. in MAICS. 2013.

Port, A.C. and R.V. Yampolskiy. Using a GA and Wisdom of Artificial Crowds to solve solitaire battleship puzzles. in Computer Games (CGAMES), 2012 17th International Conference on. 2012. IEEE.

Hughes, R. and R.V. Yampolskiy, Solving Sudoku Puzzles with Wisdom of Artificial Crowds. Int. J. Intell. Games & Simulation, 2012. 7(1): p. 24-29.

Ashby, L.H. and R.V. Yampolskiy. Genetic algorithm and Wisdom of Artificial Crowds algorithm applied to Light up. in Computer Games (CGAMES), 2011 16th International Conference on. 2011. IEEE.

Hindi, M. and R.V. Yampolskiy. Genetic Algorithm Applied to the Graph Coloring Problem. in MAICS. 2012.

Yampolskiy, R.V., Application of bio-inspired algorithm to the problem of integer factorisation. International Journal of Bio-Inspired Computation, 2010. 2(2): p. 115-123.

Mishra, M., S. Pal, and R. Yampolskiy, Nature-Inspired Computing Techniques for Integer Factorization. Evolutionary Computation: Techniques and Applications, 2016: p. 401.

Yampolskiy, R., et al. Printer model integrating genetic algorithm for improvement of halftone patterns. in Western New York Image Processing Workshop (WNYIPW). 2004. Citeseer.

Yampolskiy, R.V., Efficiency Theory: a Unifying Theory for Information, Computation and Intelligence. Journal of Discrete Mathematical Sciences and Cryptography, 2013. 16(4-5): p. 259-277.

Rylander, B., T. Soule, and J. Foster. Computational complexity, genetic programming, and implications. in European Conference on Genetic Programming. 2001. Springer.

White, D.R., et al., Better GP benchmarks: community survey results and proposals. Genetic Programming and Evolvable Machines, 2013. 14(1): p. 3-29.

Woodward, J.R. and R. Bai. Why evolution is not a good paradigm for program induction: a critique of genetic programming. in Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation. 2009. ACM.

Helmuth, T. and L. Spector. General program synthesis benchmark suite. in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. 2015. ACM.

Shulman, C. and N. Bostrom, How hard is artificial intelligence? Evolutionary arguments and selection effects. Journal of Consciousness Studies, 2012. 19(7-8): p. 103-130.

Becker, K. and J. Gottschlich, AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms. arXiv preprint arXiv:1709.05703, 2017.

Eiben, A.E. and J. Smith, From evolutionary computation to the evolution of things. Nature, 2015. 521(7553): p. 476.

Orlov, M. and M. Sipper, FINCH: A system for evolving Java (bytecode), in Genetic Programming Theory and Practice VIII. 2011, Springer. p. 1-16.

Koza, J.R., Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines, 2010. 11(3-4): p. 251-284.