Abstract
Estimation of distribution algorithms (EDA) are similar to genetic algorithms except that they replace crossover and mutation with sampling from an estimated probability distribution. We develop a framework for estimation of distribution algorithms based on the principle of maximum entropy and the conservation of schema frequencies. An algorithm of this type gives better performance than a standard genetic algorithm (GA) on a number of standard test problems involving deception and epistasis (i.e. Trap and NK).
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References
Larrañaga, P.: A review on estimation of distribution algorithms, ch. 3, pp. 57–100. Kluwer Academic Publishers, Dordrecht (2002)
de Bonet, J.S., Isbell Jr., C.L., Viola, P.: MIMIC: Finding optima by estimating probability densities. In: Mozer, M.C., et al. (eds.) Advances in Neural Information Processing Systems, vol. 9, p. 424. MIT Press, Cambridge (1997)
Jaynes, E.T.: Information theory and statistical mechanics. Physical Review 106, 620–630 (1957)
Holland, J.: Adapdation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Stephens, C.R., Waelbroeck, H.: Schemata evolution and building blocks. Evolutionary Computation 7(2), 109–124 (1999)
Poli, R.: Exact schema theory for genetic programming and variable-length genetic algorithms with one point crossover. Genetic Programming and Evolvable Machines 2(2), 123–163 (2001)
Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)
Stephens, C.R., Waelbroeck, H., Aguirre, R.: Schemata as building blocks: does size matter. In: Foundations of Genetic Algorithms 5, pp. 117–133. Morgan Kaufmann, San Francisco (1997)
Mühlenbein, H., Mahnig, T.: Convergence theory and application of the factorized distribution algorithm. Journal of Computing and Information Technology 7(1), 19–32 (1999)
Mühlenbein, H., Mahnig, T.: FDA – a scalable evolutionary algorithm for the optimization of additively decomposed functions. Evolutionary Computation 7(4), 353–376 (1999)
Mühlenbein, H.: The equation for the response to selection and its use for prediction. Evolutionary Computation 5(3), 303–346 (1997)
Heckendorn, R.E., Wright, A.H.: Efficient linkage discovery by limited probing. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1003–10014. Springer, Heidelberg (2003)
Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Foundations of Genetic Algorithms - 2, pp. 93–108. Morgan Kaufmann, San Francisco (1992)
Pelikan, M., Goldberg, D.E., Cantu-Paz, E.: Linkage problem, distribution estimation, and bayesian networks. Evolutionary Computation 8(3), 311–340 (2000)
Kauffman, S.A.: The Origins of Order. Oxford University Press, Oxford (1993)
Wright, A.H., Rowe, J.E., Poli, R., Stephens, C.R.: Bistability in a gene pool GA with mutation. In: Foundations of genetic algorithms-7, San Mateo, Morgan Kaufmann, San Francisco (2003)
Feinstein, A.: Foundations of Information Theory. The Maple Press Company, York (1959)
Guiasu, S., Shenitzer, A.: The principle of maximum entropy. The Mathematical Intelligencer 7, 42–48 (1985)
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Wright, A., Poli, R., Stephens, C.R., Langdon, W.B., Pulavarty, S. (2004). An Estimation of Distribution Algorithm Based on Maximum Entropy. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_30
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DOI: https://doi.org/10.1007/978-3-540-24855-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22343-6
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