Abstract
In the natural world, individual organisms can adapt as their environment changes. In most in silico evolution, however, individual organisms tend to consist of rigid solutions, with all adaptation occurring at the population level. If we are to use artificial evolving systems as a tool in understanding biology or in engineering robust and intelligent systems, however, they should be able to generate solutions with fitness-enhancing phenotypic plasticity. Here we use Avida, an established digital evolution system, to investigate the selective pressures that produce phenotypic plasticity. We witness two different types of fitness-enhancing plasticity evolve: static-execution-flow plasticity, in which the same sequence of actions produces different results depending on the environment, and dynamic-execution-flow plasticity, where organisms choose their actions based on their environment. We demonstrate that the type of plasticity that evolves depends on the environmental challenge the population faces. Finally, we compare our results to similar ones found in vastly different systems, which suggest that this phenomenon is a general feature of evolution.
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References
Holland, J.J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Koza, J., Keane, M., Streeter, M., Mydlowec, W., Yu, J., Lanza, G.: Genetic Programming: Routine Human-Competitive Machine Intelligence. Kluwer, New York (2003)
Stanley, K.O., Bryant, B.D., Miikkulainen, R.: Evolving Adaptive Neural Networks with and without Adaptive Synapses. In: IEEE Congress on Evolutionary Computation, Canberra, Australia, IEEE Press, Los Alamitos (2003)
Nolfi, S., Floreano, D.: Learning and Evolution. Autonomous RobotsĀ 7, 89ā113 (2004)
Ackely, D.E., Littman, M.L.: Interactions between Learning and Evolution. In: Proceedings of the Second Conference on Artificial Life, Addison-Wesley, Reading (1991)
Belew, R.K., McInerney, J., Schraudolph, N.N.: Evolving Networks: Using the Genetic Algorithm with Connectionist Learning. CSE Technical Report CS89-174. University of California, San Diego (1990)
Whiteson, S., Stone, P.: Evolutionary Function Approximation for Reinforcement Learning. Journal of Machine Learning Research, 877-917 (2006)
Nolfi, S.: Learning and Evolution in Neural Networks. Adaptive BehaviorĀ 3, 5ā28 (1994)
Baldwin, J.M.: A New Factor in Evolution. American Naturalist, 441-451 (1896)
Hinton, G.E., Nowlan, S.J.: How Learning Can Guide Evolution. Complex Systems, 495-502 (1987)
Nolfi, S., Miglino, O., Parisi, D.: Phenotypic Plasticity in Evolving Neural Networks, 146-157 (1994)
Ofria, C., Wilke, C.O.: Avida: A Software Platform for Research in Computational Evolutionary Biology. Artificial LifeĀ 10, 191ā229 (2004)
Lenski, R.E., Ofria, C., Collier, T.C., Adami, C.: Genome Complexity, Robustness and Genetic Interactions in Digital Organisms. NatureĀ 400, 661ā664 (1999)
Ofria, C., Adami, C., Collier, T.C.: Design of Evolvable Computer Languages. IEEE Transactions on Evolutionary Computation, 420-424 (2002)
Misevic, D., Ofria, C., Lenski, R.E.: Sexual Reproduction Reshapes the Genetic Architecture of Digital Organisms. Proceedings of the Royal Society London, Series BĀ 273, 457ā464 (2006)
Adami, C., Ofria, C., Collier, T.C.: Evolution of Biological Complexity. Proceedings of the National Academy of SciencesĀ 97, 4463ā4468 (2000)
Goings, S., Clune, J., Ofria, C., Pennock, R.T.: Kin-Selection: The Rise and Fall of Kin-Cheaters. In: Proceedings of Artificial Life Nine, pp. 303ā308 (2004)
Lenski, R.E., Ofria, C., Pennock, R.T., Adami, C.: The Evolutionary Origin of Complex Features. NatureĀ 423, 139ā144 (2003)
Darwin, C.: On the Various Contrivances by Which British and Foreign Orchids Are Fertilized by Insects. Murray, London (1862)
Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (1976)
Dawkins, R.: The Blind Watchmaker. Penguin, London (1986)
Gould, S.J.: The Pandaās Thumb: More Reflections in Natural History. Norton, New York (1980)
Gould, S.J., Lewontin, R.C.: The Spandrels of San Marco and the Panglossian Paradigm: A Critique of the Adaptationist Programme. Proceedings of the Royal Society of LondonĀ 205, 281ā288 (1979)
Jacob, F.: Evolution and Tinkering. Science, 1161-1166 (1977)
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Clune, J., Ofria, C., Pennock, R.T. (2007). Investigating the Emergence of Phenotypic Plasticity inĀ Evolving Digital Organisms. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds) Advances in Artificial Life. ECAL 2007. Lecture Notes in Computer Science(), vol 4648. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74913-4_8
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DOI: https://doi.org/10.1007/978-3-540-74913-4_8
Publisher Name: Springer, Berlin, Heidelberg
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