The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

Risto Miikkulainen | Christoph Adami | Marc Parizeau | Kenneth O. Stanley | Peter J. Bentley | Charles Ofria | William F. Punch | Robert T. Pennock | Hod Lipson | Westley Weimer | Stephanie Forrest | Antoine Cully | Stéphane Doncieux | Antoine Frénoy | François Taddei | Anh Nguyen | Frank Hutter | Jeff Clune | Richard E. Lenski | Joel Lehman | Laura M. Grabowski | Danesh Tarapore | Babak Hodjat | Kai Olav Ellefsen | Guillaume Beslon | David P. Parsons | Carole Knibbe | Richard Watson | Laurent Keller | Robert Feldt | Patryk Chrabaszcz | Karl Sims | Simon Thibault | David M. Bryson | Fred C. Dyer | Robert MacCurdy | Jean-Baptiste Mouret | Marc Schoenauer | Christian Gagné | Peter Krcah | David E. Moriarty | Samuel Bernard | Nick Cheney | Thomas S. Ray | Carlos Maestre | Dusan Misevic | Lee Altenberg | Julie Beaulieu | Stephan Fischer | Leni K. Le Goff | Sara Mitri | Eric Shulte | Jason Yosinksi | L. Altenberg | J. Clune | F. Hutter | Anh M Nguyen | M. Parizeau | Westley Weimer | Jean-Baptiste Mouret | C. Ofria | K. Sims | R. Miikkulainen | Antoine Cully | R. Watson | C. Adami | J. Lehman | R. Lenski | H. Lipson | S. Doncieux | S. Forrest | Marc Schoenauer | P. Bentley | Nick Cheney | D. Misevic | Julie Beaulieu | Samuel Bernard | G. Beslon | P. Chrabaszcz | F. Dyer | R. Feldt | Stephan Fischer | Antoine Frénoy | Christian Gagné | L. L. Goff | L. Grabowski | B. Hodjat | L. Keller | C. Knibbe | Peter Krcah | R. MacCurdy | Carlos Maestre | Sara Mitri | W. Punch | T. Ray | E. Shulte | F. Taddei | Danesh Tarapore | S. Thibault | Jason Yosinksi | Karl Sims | D. E. Moriarty | K. Ellefsen | R. Maccurdy | Charles Ofria

[1]  N. Pierce Origin of Species , 1914, Nature.

[2]  A. Church An Unsolvable Problem of Elementary Number Theory , 1936 .

[3]  A. Turing On computable numbers, with an application to the Entscheidungsproblem , 1937, Proc. London Math. Soc..

[4]  C. Waddington Canalization of Development and the Inheritance of Acquired Characters , 1942, Nature.

[5]  Alfred Korzybski,et al.  Science and sanity : an introduction to non-aristotelian systems and general semantics / Alfred Korzybski , 1942 .

[6]  I. Schmalhausen Factors of evolution : the theory of stabilizing selection , 1946 .

[7]  T. Eisner,et al.  Biochemistry at 100�C: Explosive Secretory Discharge of Bombardier Beetles (Brachinus) , 1969, Science.

[8]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[9]  T. Dobzhansky Chance and Creativity in Evolution , 1974 .

[10]  Donald T. Campbell,et al.  Assessing the Impact of Planned Social Change* , 2010, Journal of MultiDisciplinary Evaluation.

[11]  M. Kimura,et al.  The neutral theory of molecular evolution. , 1983, Scientific American.

[12]  S. Gould,et al.  Exaptation—a Missing Term in the Science of Form , 1982, Paleobiology.

[13]  M. Kimura The Neutral Theory of Molecular Evolution: Introduction , 1983 .

[14]  C. Goodhart Problems of Monetary Management: The UK Experience , 1984 .

[15]  V. Braitenberg Vehicles, Experiments in Synthetic Psychology , 1984 .

[16]  Yosaku Nishiwaki,et al.  Natural selection and adaptation , 1985 .

[17]  J. Endler Frequency-dependent predation, crypsis and aposematic coloration. , 1988, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[18]  P. Schuster,et al.  Stationary mutant distributions and evolutionary optimization. , 1988, Bulletin of mathematical biology.

[19]  John R. Koza,et al.  A Hierarchical Approach to Learning the Boolean Multiplexer Function , 1990, FOGA.

[20]  Christopher G. Langton,et al.  Computation at the edge of chaos: Phase transitions and emergent computation , 1990 .

[21]  J. Drake A constant rate of spontaneous mutation in DNA-based microbes. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Donald C. O'Shea Monochromatic quartet: a search for the global optimum , 1991, Other Conferences.

[23]  E. Wilson The Diversity of Life , 1992 .

[24]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[25]  Thomas S. Ray,et al.  An Evolutionary Approach to Synthetic Biology: Zen and the Art of Creating Life , 1993, Artificial Life.

[26]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[27]  P. Vandersmagt Simderella: A robot simulator for neuro-controller design , 1994 .

[28]  Karl Sims,et al.  Evolving 3D Morphology and Behavior by Competition , 1994, Artificial Life.

[29]  L. Altenberg The evolution of evolvability in genetic programming , 1994 .

[30]  Patrick van der Smagt,et al.  Simderella: a robot simulator for neuro-controller design , 1994 .

[31]  Christopher G. Langton,et al.  Artificial Life , 2019, Philosophical Posthumanism.

[32]  D. Hull Universal Darwinism , 1995, Nature.

[33]  Risto Miikkulainen,et al.  Discovering Complex Othello Strategies Through Evolutionary Neural Networks , 1995 .

[34]  D. Dennett Darwin's Dangerous Idea , 1995 .

[35]  Peter John Bentley,et al.  Generic evolutionary design of solid objects using a genetic algorithm , 2007 .

[36]  Risto Miikkulainen,et al.  Evolving Obstacle Avoidance Behavior in a Robot Arm , 1996 .

[37]  Adrian Thompson,et al.  An Evolved Circuit, Intrinsic in Silicon, Entwined with Physics , 1996, ICES.

[38]  P. Nordin,et al.  Explicitly defined introns and destructive crossover in genetic programming , 1996 .

[39]  D. Deutsch The fabric of reality , 1997, The Art of Political Storytelling.

[40]  F. Arnold,et al.  Combinatorial protein design: strategies for screening protein libraries. , 1997, Current opinion in structural biology.

[41]  M. Bedau Weak Emergence * , 1997 .

[42]  Risto Miikkulainen,et al.  Forming Neural Networks Through Efficient and Adaptive Coevolution , 1997, Evolutionary Computation.

[43]  Riccardo Poli,et al.  Fitness Causes Bloat , 1998 .

[44]  A. Fire,et al.  Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans , 1998, Nature.

[45]  F. Arnold Design by Directed Evolution , 1998 .

[46]  Gary William Flake,et al.  The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation , 1998 .

[47]  John H. Holland,et al.  Emergence. , 1997, Philosophica.

[48]  Get A Life , 1998, Science.

[49]  Robert Feldt,et al.  Generating diverse software versions with genetic programming: and experimental study , 1998, IEE Proc. Softw..

[50]  M. Huynen,et al.  Neutral evolution of mutational robustness. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[51]  Jordan B. Pollack,et al.  Embodied evolution: embodying an evolutionary algorithm in a population of robots , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[52]  E. Bornberg-Bauer,et al.  Modeling evolutionary landscapes: mutational stability, topology, and superfunnels in sequence space. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[53]  Marc Schoenauer,et al.  Evolutionary Algorithms as Fittness Function Debuggers , 1999, ISMIS.

[54]  R. Feldt Genetic Programming as an Explorative Tool in Early Software Development Phases , 1999 .

[55]  C. Schmidt-Dannert,et al.  Directed evolution of industrial enzymes. , 1999, Trends in biotechnology.

[56]  Peter J. Bentley Is evolution creative , 1999 .

[57]  C. Ofria,et al.  Genome complexity, robustness and genetic interactions in digital organisms , 1999, Nature.

[58]  M. Madigan Bacterial Habitats in Extreme Environments , 2000 .

[59]  William B. Langdon,et al.  Quadratic Bloat in Genetic Programming , 2000, GECCO.

[60]  C. Adami,et al.  Evolution of Biological Complexity , 2000, Proc. Natl. Acad. Sci. USA.

[61]  Robert T. Pennock Can Darwinian Mechanisms Make Novel Discoveries?: Learning from discoveries made by evolving neural networks , 2000 .

[62]  D. Schluter,et al.  The Ecology of Adaptive Radiation , 2000 .

[63]  E. Koonin,et al.  Genome of the Extremely Radiation-Resistant Bacterium Deinococcus radiodurans Viewed from the Perspective of Comparative Genomics , 2001, Microbiology and Molecular Biology Reviews.

[64]  C. Ofria,et al.  Evolution of digital organisms at high mutation rates leads to survival of the flattest , 2001, Nature.

[65]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[66]  Jordan B. Pollack,et al.  Embodied Evolution: Distributing an evolutionary algorithm in a population of robots , 2002, Robotics Auton. Syst..

[67]  M. Pagel Encyclopedia of evolution , 2002 .

[68]  J. Launer Darwin's dangerous idea. , 2002, QJM : monthly journal of the Association of Physicians.

[69]  Robert Feldt Biomimetic Software Engineering Techniques for Dependability , 2002 .

[70]  G. Yedid,et al.  Macroevolution simulated with autonomously replicating computer programs , 2002, Nature.

[71]  Robert T. Pennock,et al.  The evolutionary origin of complex , 2003 .

[72]  M. Bedau Artificial life: organization, adaptation and complexity from the bottom up , 2003, Trends in Cognitive Sciences.

[73]  Robert T. Pennock,et al.  The evolutionary origin of complex features , 2003, Nature.

[74]  N. Lau,et al.  Censors of the genome. , 2003, Scientific American.

[75]  M. Kundrát When did theropods become feathered?--evidence for pre-Archaeopteryx feathery appendages. , 2004, Journal of experimental zoology. Part B, Molecular and developmental evolution.

[76]  Charles Ofria,et al.  Avida , 2004, Artificial Life.

[77]  K. Foster,et al.  Pleiotropy as a mechanism to stabilize cooperation , 2004, Nature.

[78]  M. Eigen Selforganization of matter and the evolution of biological macromolecules , 1971, Naturwissenschaften.

[79]  C. Ofria,et al.  Adaptive Radiation from Resource Competition in Digital Organisms , 2004, Science.

[80]  Risto Miikkulainen,et al.  Real-time neuroevolution in the NERO video game , 2005, IEEE Transactions on Evolutionary Computation.

[81]  C. Ofria,et al.  Sexual reproduction reshapes the genetic architecture of digital organisms , 2006, Proceedings of the Royal Society B: Biological Sciences.

[82]  U. Alon,et al.  Spontaneous evolution of modularity and network motifs. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[83]  Sean Luke,et al.  A Comparison of Bloat Control Methods for Genetic Programming , 2006, Evolutionary Computation.

[84]  Jeffrey E. Barrick,et al.  Balancing Robustness and Evolvability , 2006, PLoS biology.

[85]  C. Adami Digital genetics: unravelling the genetic basis of evolution , 2006, Nature Reviews Genetics.

[86]  G. Wagner,et al.  The road to modularity , 2007, Nature Reviews Genetics.

[87]  Kenneth DeJong Evolutionary computation: a unified approach , 2007, GECCO.

[88]  D. Floreano,et al.  Evolutionary Conditions for the Emergence of Communication in Robots , 2007, Current Biology.

[89]  Dan S. Tawfik,et al.  Protein engineers turned evolutionists , 2007, Nature Methods.

[90]  Colin R. Reeves,et al.  Evolutionary computation: a unified approach , 2007, Genetic Programming and Evolvable Machines.

[91]  Uri Alon,et al.  Varying environments can speed up evolution , 2007, Proceedings of the National Academy of Sciences.

[92]  G. Beslon,et al.  A long-term evolutionary pressure on the amount of noncoding DNA. , 2007, Molecular biology and evolution.

[93]  Robert T. Pennock Models, simulations, instantiations, and evidence: the case of digital evolution , 2007, J. Exp. Theor. Artif. Intell..

[94]  Marc Parizeau,et al.  Human-competitive lens system design with evolution strategies , 2008, Appl. Soft Comput..

[95]  Peter Krcah,et al.  Towards Efficient Evolutionary Design of Autonomous Robots , 2008, ICES.

[96]  Ernesto Costa,et al.  Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories , 2009, Genetic Programming and Evolvable Machines.

[97]  Dario Floreano,et al.  Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios , 2008, ALIFE.

[98]  J. Clune,et al.  Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes , 2008, PLoS computational biology.

[99]  Charles Ofria,et al.  Cockroaches, drunkards, and climbers: Modeling the evolution of simple movement strategies using digital organisms , 2009, 2009 IEEE Symposium on Artificial Life.

[100]  Sam P. Brown,et al.  Horizontal Gene Transfer of the Secretome Drives the Evolution of Bacterial Cooperation and Virulence , 2009, Current Biology.

[101]  Chapter 3 Invasion of the Body Snatchers , 2009 .

[102]  Claire Le Goues,et al.  A genetic programming approach to automated software repair , 2009, GECCO.

[103]  D. Floreano,et al.  The evolution of information suppression in communicating robots with conflicting interests , 2009, Proceedings of the National Academy of Sciences.

[104]  Leslie G. Valiant,et al.  Evolvability , 2009, JACM.

[105]  T. Lefèvre,et al.  Invasion of the body snatchers: the diversity and evolution of manipulative strategies in host-parasite interactions. , 2009, Advances in parasitology.

[106]  A. E. Eiben,et al.  On-Line, On-Board Evolution of Robot Controllers , 2009, Artificial Evolution.

[107]  Westley Weimer,et al.  Automated program repair through the evolution of assembly code , 2010, ASE.

[108]  Carol E. Cleland,et al.  The Nature of Life: Classical and Contemporary Perspectives from Philosophy and Science , 2010 .

[109]  Kenneth O. Stanley,et al.  Abandoning Objectives: Evolution Through the Search for Novelty Alone , 2011, Evolutionary Computation.

[110]  Kenneth O. Stanley,et al.  Improving evolvability through novelty search and self-adaptation , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[111]  Kenneth O. Stanley,et al.  Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space , 2011, Evolutionary Computation.

[112]  Riccardo Poli,et al.  Neutrality in evolutionary algorithms… What do we know? , 2011, Evol. Syst..

[113]  Antoine Frénoy,et al.  Robustness and evolvability of cooperation , 2012, ALIFE.

[114]  G. Beslon,et al.  New insights into bacterial adaptation through in vivo and in silico experimental evolution , 2012, Nature Reviews Microbiology.

[115]  M. Runco,et al.  The Standard Definition of Creativity , 2012 .

[116]  Antoine Frénoy,et al.  Effects of public good properties on the evolution of cooperation , 2012, ALIFE.

[117]  C. Ofria,et al.  Task-switching costs promote the evolution of division of labor and shifts in individuality , 2012, Proceedings of the National Academy of Sciences.

[118]  N. Roese,et al.  Hindsight Bias , 2012 .

[119]  Yuanyuan Zhang,et al.  Search-based software engineering: Trends, techniques and applications , 2012, CSUR.

[120]  Arthur W. Covert,et al.  Experiments on the role of deleterious mutations as stepping stones in adaptive evolution , 2013, Proceedings of the National Academy of Sciences.

[121]  Una-May O'Reilly,et al.  EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System , 2013 .

[122]  Robert T. Pennock,et al.  A Case Study of the De Novo Evolution of a Complex Odometric Behavior in Digital Organisms , 2013, PloS one.

[123]  Westley Weimer,et al.  Advances in Automated Program Repair and a Call to Arms , 2013, SSBSE.

[124]  Hod Lipson,et al.  Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding , 2013, GECCO '13.

[125]  Antoine Frénoy,et al.  Genetic Architecture Promotes the Evolution and Maintenance of Cooperation , 2013, PLoS Comput. Biol..

[126]  Hod Lipson,et al.  The evolutionary origins of modularity , 2012, Proceedings of the Royal Society B: Biological Sciences.

[127]  Christian Rosenmund,et al.  Ultrafast endocytosis at mouse hippocampal synapses , 2013, Nature.

[128]  Samuel Bernard,et al.  A Model for Genome Size Evolution , 2014, Bulletin of Mathematical Biology.

[129]  Nick Bostrom,et al.  Superintelligence: Paths, Dangers, Strategies , 2014 .

[130]  Antoine Cully,et al.  Robots that can adapt like animals , 2014, Nature.

[131]  Jean-Baptiste Mouret,et al.  Illuminating search spaces by mapping elites , 2015, ArXiv.

[132]  Jean-Baptiste Mouret,et al.  Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills , 2015, PLoS Comput. Biol..

[133]  Kenneth O. Stanley,et al.  Investigating Biological Assumptions through Radical Reimplementation , 2015, Artificial Life.

[134]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[135]  Michael G Schöner,et al.  Bats Are Acoustically Attracted to Mutualistic Carnivorous Plants , 2015, Current Biology.

[136]  P. Ecarlat,et al.  Learning a high diversity of object manipulations though an evolutionary-based babbling , 2015 .

[137]  A Nguyen,et al.  Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning , 2016, Evolutionary Computation.

[138]  Sebastian Risi,et al.  WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web , 2016, Artificial Life.

[139]  Alex Graves,et al.  Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.

[140]  K. Last,et al.  Moonlight Drives Ocean-Scale Mass Vertical Migration of Zooplankton during the Arctic Winter , 2016, Current Biology.

[141]  Richard McElreath,et al.  The natural selection of bad science , 2016, Royal Society Open Science.

[142]  Marcus Hutter,et al.  Avoiding Wireheading with Value Reinforcement Learning , 2016, AGI.

[143]  G. Wagner,et al.  Resolving the paradox of evolvability with learning theory: How evolution learns to improve evolvability on rugged fitness landscapes , 2016, 1612.05955.

[144]  John Schulman,et al.  Concrete Problems in AI Safety , 2016, ArXiv.

[145]  Xi Chen,et al.  Evolution Strategies as a Scalable Alternative to Reinforcement Learning , 2017, ArXiv.

[146]  Kenneth O. Stanley,et al.  Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning , 2017, ArXiv.

[147]  Markus Brede,et al.  How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation , 2017, PLoS Comput. Biol..

[148]  Frank Hutter,et al.  Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari , 2018, IJCAI.

[149]  Marcus Hutter,et al.  AGI Safety Literature Review , 2018, IJCAI.

[150]  Laurent Orseau,et al.  Measuring and avoiding side effects using relative reachability , 2018, ArXiv.

[151]  J. Clune,et al.  The Surprising Creativity of Digital Evolution , 2018, ALIFE.

[152]  Joel Lehman,et al.  Evolutionary Computation and AI Safety: Research Problems Impeding Routine and Safe Real-world Application of Evolution , 2019, GPTP.

[153]  Jessica Taylor,et al.  Alignment for Advanced Machine Learning Systems , 2020, Ethics of Artificial Intelligence.