Abstract
In this paper, we add learning behavior to artificial evolution simulation and evaluate the effect of learning behavior. Each individual establishes its own neural network with its genetic information. Also, we propose a reward function to take reinforcement learning in a complicated and dynamically-determined environment. When the individual-level learning behavior was introduced, evolution of each simulation model got faster and the effectiveness of evolution was significantly improved. But the direction of evolution did not depend on learning and it was possible to affect the forms of evolution through reinforcement learning. This provides the mechanism that can apply the artificial life technique to various fields.
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References
Baldwin, J. M.: A new factor in evolution, in: R. K. Belew and M. Mitchell (eds), Adaptive Individuals in Evolving Populations: Models and Algorithms, Addison-Wesley, Reading, MA, 1996.
Collins, R. J.: Studies in artificial evolution, PhD Thesis (Philosophy in Computer Science), University of California, Los Angeles, 1992.
Collins, R. J. and Jefferson, D. R.: AntFarm: Towards simulated evolution, in: J. D. Farmer, C. Langton, S. Rasmussen, and C. Taylor (eds), Artificial Life II, Addison-Wesley, Reading, MA, 1991.
Filho, J. R., Alippi, C., and Treleaven, P.: Genetic algorithm programming environment, IEEE Computer Journal(1991).
Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.
Gruau, F. and Whitley, D.: Adding learning to the cellular development of neural networks: Evolution and the Baldwin effect, in: Evolutionary Computation I, Vol. 3, 1993, pp. 213-233.
Holland, J. H.: Adaptation in Natural and Artificial Systems, Univ. of Michigan Press, reprinted by MIT Press, 1992.
Kaelbling, L. P., Littman, M. L., and Moore, A. W.: Reinforcement learning: A survey, J. Artificial Intelligence Res. 4, AI Access Foundation and Morgan Kaufmann Publishers (1996).
Koza, J. R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
Langton, C.: Studying artificial life with cellular automata, Phys. D 22(1986), 120-149.
Langton, C.: in: C. Langton (ed.), Artificial Life II, Addison-Wesley, Reading, MA, 1989, pp. 1-47.
Lin, C. T. and Lee, G.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent System, Prentice-Hall, Englewood Cliffs, NJ, 1996.
Nolfi, S., Elman, J. L., and Parisi, D.: Learning and evolution in neural networks. CRL Technical Report 9019, University of California, San Diego, 1990.
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Lee, MR., Rhee, H. The Effect of Evolution in Artificial Life Learning Behavior. Journal of Intelligent and Robotic Systems 30, 399–414 (2001). https://doi.org/10.1023/A:1011131420988
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DOI: https://doi.org/10.1023/A:1011131420988