Abstract
A new class of estimation of distribution algorithms (EDAs), known as cellular EDAs (cEDAs), has recently emerged. In these algorithms, the population is decentralized by partitioning it into many small collaborating subpopulations, arranged in a toroidal grid, and interacting only with its neighboring subpopulations. In this work, we study the simplest cEDA —the cellular univariate marginal distribution algorithm (cUMDA). In an attempt to explain its behaviour, we extend the well known takeover time analysis usually applied to other evolutionary algorithms to the field of EDAs. We also give in this work empirical arguments in favor of using the cUMDAs instead of its centralized equivalent.
E. Alba, and B. Dorronsoro acknowledge the support by FEDER and MEC under contract TIN2005-08818-C04-01 (the OPLINK project: http://oplink.lcc.uma.es).
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References
Bäck, T., Fogel, D., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)
Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular evolutionary algorithms. IEEE TEC 9(2), 126–142 (2005)
Mühlenbein, H., Paab, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996)
Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2002)
Cantú-Paz, E.: Feature subset selection by estimation of distribution algorithms. In: GECCO, pp. 303–310. Morgan Kaufmann, San Francisco (2002)
Pelikan, M., Mühlenbein, H.: The bivariate marginal distribution algorithm. Advances in Soft Computing-Engineering Design and Manufacturing, 521–535 (1999)
Alba, E., Troya, J.: Improving flexibility and efficiency by adding parallelism to genetic algorithms. Statistics and Computing 12(2), 91–114 (2002)
Madera, J., Alba, E., Ochoa, A.: Parallel Estimation of Distribution Algorithms. In: Alba, E. (ed.) Parallel Metaheuristics: A New Class of Algorithms, pp. 203–222. John Wiley & Sons, Chichester (2005)
Ochoa, A., Soto, M., Alba, E.: Cellular estimation of distribution algorithms (2006) (in preparation)
Bosman, P., Thierens, D.: Advancing continuous IDEAs with mixture distributions and factorization selection metrics. In: OBUPM, pp. 208–212 (2001)
Spiessens, P., Manderick, B.: A massively parallel genetic algorithm. In: Belew, R., Booker, L. (eds.) 4th ICGA, pp. 279–286. Morgan Kaufmann, San Francisco (1991)
Baluja, S.: Structure and performance of fine-grain parallelism in genetic search. In: Forrest, S. (ed.) 6th ICGA, pp. 155–162. Morgan Kaufmann, San Francisco (1993)
Mühlenbein, H., Schomish, M., Born, J.: The parallel genetic algorithm as a function optimizer. Parallel Computing 17, 619–632 (1991)
Manderick, B., Spiessens, P.: Fine-grained parallel genetic algorithm. In: 3rd ICGA, pp. 428–433 (1989)
Giacobini, M., Alba, E., Tomassini, M.: Selection intensity in asynchronous cellular evolutionary algorithms. In: GECCO, pp. 955–966. Springer, Heidelberg (2003)
Schaffer, J., Eshelman, L.: On crossover as an evolutionary viable strategy. In: 4th ICGA, pp. 61–68. Morgan Kaufmann, San Francisco (1991)
Mühlenbein, H., Schlierkamp-Voosen, D.: The science of breeding and its application to the breeder genetic algorithm (BGA). Evol. Comp. 1, 335–360 (1993)
Mahnig, T., Mühlenbein, H.: Comparing the adaptive Boltzmann selection schedule SDS to truncation selection. In: CIMAF, pp. 121–128 (1999)
Jong, K.D., Potter, M., Spears, W.: Using problem generators to explore the effects of epistasis. In: 7th ICGA, pp. 338–345. Morgan Kaufmann, San Francisco (1997)
Stinson, D.R.: An Introduction to the Design and Analysis of Algorithms. The Charles Babbage Research Center, Canada (1985) (second edition, 1987)
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Alba, E., Madera, J., Dorronsoro, B., Ochoa, A., Soto, M. (2006). Theory and Practice of Cellular UMDA for Discrete Optimization. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_25
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DOI: https://doi.org/10.1007/11844297_25
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