Abstract
In this paper, we investigate the effectiveness of several techniques commonly recommended for overcoming convergence problems with coevolutionary algorithms. In particular, we investigate effects of the Hall of Fame, and of several diversity maintenance methods, on a problem designed to test the ability of coevolutionary algorithms to deal with an intransitive superiority relation between solutions. We measure and analyse the effects of these methods on population diversity and on solution quality.
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References
Axelrod, R.: The evolution of strategies in the iterated Prisoner’s Dilemma. Genetic Algorithms and Simulated Annealing, 32–41 (1987)
de Jong, E., Stanley, K., Wiegand, P.: Introductory tutorial on coevolution. In: Proceedings of the 2007 Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 3133–3157. ACM, New York (2007)
Ficici, S.G.: Solution concepts in coevolutionary algorithms. Ph.D. Dissertation. Brandeis University (2004)
Hillis, W.D.: Coevolving parasites improve simulated evolution as an optimization procedure. Physica D: Nonlinear Phenomena 42, 228–234 (1990)
Porter, M.A., de Jong, K.A.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
Rosin, C.D.: Coevolutionary search among adversaries. Ph.D. Dissertation. University of California, San Diego (1997)
Wiegand, R.P.: An analysis of cooperative coevolutionary algorithms. George Mason University, Virginia (2003)
Rosin, C.D., Belew, R.K.: New methods for competitive coevolution. Evolutionary Computation 5, 1–29 (1997)
Chong, S.Y., Tino, P., Yao, X.: Relationship between generalization and diversity in coevolutionary learning. IEEE Transactions on Computational Intelligence and AI in Games 1, 214–232 (2009)
Mckay, R.I.: Fitness sharing in genetic programming. In: Proceedings of the Proceedings of the Genetic and Evolutionary Computation Conference, Las Vegas (2000)
Ray, T.S.: Evolution, complexity, entropy and artificial reality. Physica D: Nonlinear Phenomena, 239–263 (1993)
Rosca, J.P.: Entropy-driven adaptive representation. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pp. 23–32 (1995)
Yao, X., Liu, Y.: How to Make Best Use of Evolutionary Learning. Complex Systems - From Local Interactions to Global Phenomena, 229–242 (1996)
Chong, S.Y., Tino, P., Yao, X.: Measuring Generalization Performance in Coevolutionary Learning. IEEE Transactions on Evolutionary Computation 12, 479–505 (2008)
Casillas, J., Cordon, O., Herrera, F., Merelo, J.J.: A cooperative coevolutionary algorithm for jointly learning fuzzy rule bases and membership functions. Artificial Evolution, 1075–1105 (2002)
Ficici, S.G., Pollack, J.B.: Pareto Optimality in Coevolutionary Learning. In: Kelemen, J., Sosík, P. (eds.) ECAL 2001. LNCS (LNAI), vol. 2159, pp. 316–325. Springer, Heidelberg (2001)
Watson, R.A., Pollack, J.B.: Coevolutionary dynamics in a minimal substrate. In: Proceedings of the Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2001. Morgan Kaufmann, San Francisco (2001)
Deb, K., Goyal, M.: A combined genetic adaptive search (gene AS) for Engineering Design. Computer Science and Informatics 26, 30–45 (1996)
Barker, J.E.: Adaptive Selection Methods for Genetic Algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, Hillsdale, NJ, pp. 101–111 (1985)
Poli, R., Langdon, W.B.: A new schema theorem for genetic programming with one-point crossover and point mutation. Evolutionary Computation 6, 231–252 (1998)
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Ranjeet, T.R., Masek, M., Hingston, P., Lam, CP. (2011). The Effects of Diversity Maintenance on Coevolution for an Intransitive Numbers Problem. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_34
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DOI: https://doi.org/10.1007/978-3-642-25832-9_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25831-2
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