Abstract
In this paper we evaluate on-the-fly population (re)sizing mechanisms for evolutionary algorithms (EAs). Evaluation is done by an experimental comparison, where the contestants are various existing methods and a new mechanism, introduced here. These comparisons consider EA performance in terms of success rate, speed, and solution quality, measured on a variety of fitness landscapes. These landscapes are created by a generator that allows for gradual tuning of their characteristics. Our test suite covers a wide span of landscapes ranging from a smooth one-peak landscape to a rugged 1000-peak one. The experiments show that the population (re)sizing mechanisms exhibit significant differences in speed, measured by the number of fitness evaluations to a solution and the best EAs with adaptive population resizing outperform the traditional genetic algorithm (GA) by a large margin.
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References
Arabas, J., Michalewicz, Z., Mulawka, J.: GAVaPS – a genetic algorithm with varying population size. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 73–78. IEEE Press, Piscataway (1994)
Bäck, T., Eiben, A.E., van der Vaart, N.A.L.: An empirical study on GAs without parameters. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 315–324. Springer, Heidelberg (2000)
Costa, J., Tavares, R., Rosa, A.: An experimental study on dynamic random variation of population size. In: Proc. IEEE Systems, Man and Cybernetics Conf., Tokyo, vol. 6, pp. 607–612. IEEE Press, Los Alamitos (1999)
Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)
Eiben, A.E., Jelasity, M.: A critical note on experimental research methodology in EC. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 582–587. IEEE Press, Los Alamitos (2002)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Forrest, S. (ed.): Proceedings of the 5th International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco (1993)
Goldberg, D.E.: Optimal population size for binary-coded genetic algorithms. TCGA Report, No. 85001 (1985)
Goldberg, D.E.: Sizing populations for serial and parallel genetic algorithms. In: Schaffer, J.D. (ed.) Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 70–79. Morgan Kaufmann, San Francisco (1989)
Goldberg, D.E., Deb, K., Clark, J.H.: Genetic Algorithms, Noise, and the Sizing of Populations. IlliGAL Report, No. 91010 (1991)
Hansen, N., Gawelczyk, A., Ostermeier, A.: Sizing the population with respect to the local progress in (1,λ)-evolution strategies – a theoretical analysis. In: Proceedings of the 1995 IEEE Conference on Evolutionary Computation, pp. 80–85. IEEE Press, Piscataway (1995)
Harik, G.R., Lobo, F.G.: A parameter-less genetic algorithm. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, vol. 1, pp. 258–265. Morgan Kaufmann, San Francisco (1999)
Hinterding, R., Michalewicz, Z., Peachey, T.C.: Self-adaptive genetic algorithm for numeric functions. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 420–429. Springer, Heidelberg (1996)
Lobo, F.G.: The parameter-less Genetic Algorithm: rational and automated parameter selection for simplified Genetic Algorithm operation. PhD thesis, Universidade de Lisboa (2000)
Michalewicz, Z.: Genetic Algorithms + Data structures = Evolution programs, 3rd edn. Springer, Heidelberg (1996)
Reeves, C.R.: Using genetic algorithms with small populations. In: Forrest [7], pp. 92–99.
Roughgarden, J.: Theory of Population Genetics and Evolutionary Ecology. Prentice-Hall, Englewood Cliffs (1979)
Schlierkamp-Voosen, D., Mühlenbein, H.: Adaptation of population sizes by competing subpopulations. In: Proceedings of the 1996 IEEE Conference on Evolutionary Computation, IEEE Press, Piscataway (1996)
Smith, R.E.: Adaptively resizing populations: An algorithm and analysis. In: Forrest [7]
Smith, R.E.: Population sizing, pp. 134–141. Institute of Physics Publishing (2000)
Song, J., Yu, J.: Population system control. Springer, Heidelberg (1988)
Spears, W.M.: Evolutionary Algorithms: the role of mutation and recombination. Springer, Heidelberg (2000)
Valkó, V.A.: Self-calibrating evolutionary algorithms: Adaptive population size. Master’s thesis, Free University Amsterdam (2003)
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Eiben, A.E., Marchiori, E., Valkó, V.A. (2004). Evolutionary Algorithms with On-the-Fly Population Size Adjustment. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_5
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DOI: https://doi.org/10.1007/978-3-540-30217-9_5
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