Abstract
The population size of genetic algorithms (GAs) affects the quality of the solutions and the time required to find them. While progress has been made in estimating the population sizes required to reach a desired solution quality for certain problems, in practice the sizing of populations is still usually performed by trial and error. These trials might lead to find a population that is large enough to reach a satisfactory solution, but there may still be opportunities to optimize the computational cost by reducing the size of the population. This paper presents a technique called plague that periodically removes a number of individuals from the population as the GA executes. Recently, the usefulness of the plague has been demonstrated for genetic programming. The objective of this paper is to extend the study of plagues to genetic algorithms. We experiment with deceptive trap functions, a tunable difficult problem for GAs, and the experiments show that plagues can save computational time while maintaining solution quality and reliability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Goldberg, D.E., Deb, K., Clark, J.H.: Genetic algorithms, noise, and the sizing of populations. Complex Systems 6, 333–362 (1992)
Harik, G., Cantú-Paz, E., Goldberg, D., Miller, B.L.: The gambler’s ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation 7, 231–253 (1999)
Fernandez, F.: Estudio de poblaciones de tamaño variable en programacion genetica. Actas del 2°Congreso Español obre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados, Maeb 2003, 424–428 (2003)
Fernandez, F., Tomassini, M., Saving, L.V.: computational effort in genetic programming by means of plagues. In: Proceeding of the Conference on Evolutionary Computation 2003, pp. 2042–2049. IEEE Press, Los Alamitos (2003)
Fernandez, F., Vanneschi, L., Tomassini, M.: The effect of plagues in genetic programming: a study of variable-size populations. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 317–326. Springer, Heidelberg (2003)
Goldberg, D.E.: Sizing populations for serial and parallel genetic algorithms. In: Schaffer, J.D. (ed.) International Conference on Genetic Algorithms (ICGA), San Mateo, CA, pp. 70–79. Morgan Kaufmann, San Francisco (1989)
Goldberg, D.E., Rudnick, M.: Genetic algorithms and the variance of fitness. Complex Systems 5, 265–278 (1991)
Harik, G., Cantú-Paz, E., Goldberg, D.E., Miller, B.L.: The gambler’s ruin problem, genetic algorithms, and the sizing of populations. In: International Conference on Evolutionary Computation, Piscataway, NJ, pp. 7–12. IEEE, Los Alamitos (1997)
Kirley, M., Li, X., Green, D.G.: Investigation of a cellular genetic algorithm that mimics landscape ecology. In: McKay, B., Yao, X., Newton, C.S., Kim, J.-H., Furuhashi, T. (eds.) SEAL 1998. LNCS (LNAI), vol. 1585, pp. 90–97. Springer, Heidelberg (1999)
Smith, R.E., Smuda, E.: Adaptively resizing populations: Algorithm, analysis, and first results. Complex Systems 9, 47–72 (1995)
Tan, K., Lee, T., Khor, E.: Evolutionary Algorithms with Dynamic Population Size and Local Exploration for Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 5, 565–588 (2001)
Banzhaf, W., Langdon, W.B.: Some considerations on the reason for bloat. Genetic Programming and Evolvable Machines 3, 81–91 (2002)
Monsieurs, P., Flerackers, E.: Reducing population size while maintaining diversity. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 142–152. Springer, Heidelberg (2003)
Luke, S., Balan, G.C., Panait, L.: Population implosion in genetic programming. 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. 1729–1739. Springer, Heidelberg (2003)
Fernandez de Vega, F.: Distributed Genetic Programming Models with Application to Logic Synthesis on FPGAs. PhD thesis, University of Extremadura (2001)
Fernandez, F., Tomassini, M., Vanneschi, L.: An empirical study of multipopulation genetic programming. Genetic Programming and Evolvable Machines 4, 21–51 (2003)
Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms 2, San Mateo, CA, pp. 93–108. Morgan Kaufmann, San Francisco (1993)
Wall, M.: Galib 2.3.2 (1995)
Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Accademic Publishers, Dordrecht (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Vega, F.F., Cantú-Paz, E., López, J.I., Manzano, T. (2004). Saving Resources with Plagues in Genetic Algorithms. 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_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-30217-9_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
Online ISBN: 978-3-540-30217-9
eBook Packages: Springer Book Archive