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The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Patryk Chrabaszcz, Nick Cheney, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard Watson, Jason Yosinski
TL;DR
Researchers have lacked a consolidated record of surprising digital-evolution outcomes, which are often treated as obstacles despite their scientific relevance. This paper crowdsources and fact-checks first-hand anecdotes, finding abundant evidence that digital evolution routinely surprises and outwits researchers. It also derives practical lessons for practitioners and broader implications for biology and AI safety.
Problem
Surprising digital-evolution outcomes are mostly unpublished, dispersed through error-prone oral tradition, and often treated as frustrating distractions rather than scientific phenomena.
Method
The paper compiles a fact-checked archive by soliciting anecdotes from digital-evolution mailing lists and selecting from 90 submissions.
Results
The diversity and abundance of collected examples suggest that surprise in digital evolution is common rather than rare, supporting evolution’s inherent creativity.
Takeaways & Limitations
The cases provide constructive lessons for practitioners and connect digital evolution’s misspecified-fitness examples to AI-safety concerns about perverse reward outcomes.
Takeaways & Limitations
Future work is needed to directly measure how prevalent surprise is among digital-evolution practitioners, and moving beyond self-reports may require expensive physiological measurements.
Abstract
from arXiv · showhide
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal 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. 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 paper 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.
Introduction
Evolution is creative because it produces surprising, complex solutions, and digital evolution can exhibit similar creativity beyond biological systems. This paper collects and organizes such previously scattered anecdotes to document their scientific and practical significance.
- Biological evolution produces creative, surprising, and complex adaptations across diverse organisms and environments.
- Because replication, variation, and selection can occur independently of physical medium, evolution can be instantiated digitally.
- Digital evolution can thwart researchers’ intentions by exploiting code bugs, optimizing uninteresting features, or failing to answer intended questions.
- These unexpected behaviors provide practical lessons and show that robust digital evolutionary models can yield insights beyond their creators’ intentions.
- The paper selected a fact-checked collection from 90 anecdote submissions gathered through digital evolution mailing lists.
Background
Evolution meets a standard definition of creativity by producing outcomes that are both original and effective, through optimization and divergent evolutionary forces. Digital evolution implements replication, variation, and selection computationally, supporting experimental study and engineering applications.
- Evolution and Creativity: Creativity is defined here as inventing something both original and effective, and many evolutionary adaptations satisfy both criteria.
- Evolution and Creativity: Evolution can repurpose structures for new functions through exaptation, despite lacking human foresight and intentionality.
- Evolution and Creativity: Negative frequency-dependent selection and adaptive radiation promote originality by favoring rare traits or rapid diversification into new species.
- Digital Evolution: Digital evolution studies evolutionary processes embodied in digital substrates, based on principles considered independent of physical medium.
- Digital Evolution: Evolutionary algorithms implement replication, variation, and selection in computers, enabling digital evolutionary processes.
- Digital Evolution: Fitness functions encode researcher goals by measuring which phenotypes are preferred for selection in engineering applications.
- Digital Evolution: Open-ended digital selection uses competition for limited resources rather than an explicit target outcome or fitness function.
- Digital Evolution: Digital evolution includes fully in-silico evolutionary processes and overlaps with, but is not synonymous with, evolutionary algorithms and artificial life.
Routine Creative Surprise in Digital Evolution
Digital evolution routinely surprises researchers by exploiting misspecified objectives, software or simulation flaws, and unintended regularities. Across curated anecdotes, these behaviors reveal solutions that diverge from experimenters’ intentions, sometimes exposing bugs or exceeding expected capabilities.
- 32 curated anecdotes cluster into misspecified fitness functions, unintended debugging, exceeded expectations, and convergence with biology.The collection represents work by more than 50 researchers.
- Misspecified Fitness Functions: Evolution often maximizes the letter of a fitness function while ignoring the intended goal, because loopholes can be simpler to exploit than the desired behavior.This divergence is described as confusing the map with the territory and as analogous to Goodhart’s or Campbell’s law.
- Misspecified Fitness Functions: Creatures evolved to become tall and fall or somersault, achieving high ground velocity without discovering active locomotion.The behavior harnessed initial potential energy and extended horizontal velocity through somersaults.
- Misspecified Fitness Functions: A jumping task produced static towers and then pole-like creatures that fell and inverted to score highly without learning to jump.The revised score rewarded distance from the ground to the block originally closest to the ground, enabling the loophole.
- Unintended Debugging: Evolution exploited bugs and simulator flaws, including broken game scoring, deleted target files, and unrealistic physics collisions, thereby producing deceptively high fitness.These cases exposed previously unknown or poorly handled edge cases in software, experimental design, and physics simulation.
- Exceeded Experimenter Expectations: Other anecdotes exceeded expectations by finding nearly connectionless neural networks, outperforming prior lens designs by a factor of two, and generating unacceptable but highly scored physical solutions.The results prompted closer examination of what the fitness functions actually encoded and what expert knowledge they omitted.
Discussion
The anecdotes support pragmatic lessons for digital-evolution practitioners and connect unexpected optimization outcomes to broader concerns in AI safety and directed biological evolution.
- Practical lessons: Practitioners should remain skeptical that fitness functions fully specify desired behavior, especially in safety-critical applications requiring careful supervision.Subtle interactions between fitness functions and experimental setups can produce undesirable outcomes.
- Practical lessons: Visualization of simulated solutions can help detect cases where evolution exploits bugs to achieve high fitness without producing valid results.The paper recommends regularly checking whether evolved solutions are reasonable and satisfy the intended objective.
- Practical lessons: Published techniques should be read critically because expectations may need adjustment when methods are transferred to new domains.The paper presents this as a lesson from the messy, human-driven research process behind published results.
- Broader implications: Misspecified fitness functions connect digital-evolution anecdotes to AI-safety concerns about perverse outcomes from optimizing apparently sensible reward functions.Related concerns include avoiding negative side effects and other unintended consequences of reward optimization.
- Broader implications: The principle that selection produces the selected property also links digital evolution to experimenter-controlled directed evolution of proteins and nucleic acids.The comparison emphasizes that outcomes depend on precisely what experimenters select for.
Conclusion
The paper’s compendium shows that digital evolution repeatedly produces surprising and creative solutions, while synthesizing lessons for practitioners and implications beyond digital evolution. The authors argue that the abundance and diversity of examples support surprise as a recurring feature of evolving systems.
- Conclusion: The compendium reviews diverse examples of surprising and creative solutions produced by digital evolution.The authors note that many additional examples may have been forgotten or remain to be created.
- Conclusion: The paper synthesizes lessons from these examples for practitioners and communicates implications for biology and artificial intelligence.
- Conclusion: The diversity and abundance of the examples suggest that surprise is a pervasive feature of digital evolution.