Abstract
Controlling parameters during execution of parallel evolutionary algorithms is an open research area. Some recent research have already shown good results applying self-calibrating strategies. The motivation of this work is to improve the search of parallel genetic algorithms using monitoring techniques. Monitoring results guides the algorithm to take some actions based on both the search state and the values of its parameters. In this paper, we propose a parameter control architecture for parallel evolutionary algorithms, based on self-adaptable monitoring techniques. Our approach provides an efficient and low cost monitoring technique to design parameters control strategies. Moreover, it is completely independant of the implementation of the evolutionary algorithm.
Supported by Fondecyt Project 1040364.
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
Boutros, C., Bonnaire, X.: Cluster Monitoring Platform Based on Self-Adaptable Probes. In: Proceedings of the 12th Symposium on Computer Architecture and High Performance Computing (2000)
Boutros, C., Bonnaire, X., Folliot, B.: Flexible Monitoring Platform to Build Cluster Management Services. In: Proceedings of the IEEE International Conference on Cluster Computing- CLUSTER 2000 (2000)
Cantu-Paz, E.: Dessigning efficient and accurate parallel genetic algorithms. PhD Thesis, University of Illinois at Urbana Champaign (1999)
Davis, L.: Adapting Operator Probabilities in Genetic Algorithms. In: Proceedings of 3rd. International Conf. on Genetic Algorithms and their Applications (1989)
Deb, K., Agrawal, S.: Understanding Interactions among Genetic Algorithms Parameters. Foundations of Genetic Algorithms 5, 265–286 (1998)
Eiben, A., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on evolutionary computation 3(2), 124–141 (1999)
Eiben, A.E., Marchiori, E., Valko, V.A.: Evolutionary Algorithms with on-the-fly Population Size Adjustment. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 41–50. Springer, Heidelberg (2004)
Hinterding, R., Michalewicz, Z., Eiben, A.: Adaptation in Evolutionary Computation: A Survey. In: Proceedings of 4th. IEEE International Conf. on Evolutionary Computation (1997)
Lis, J.: Parallel Genetic Algorithm with the Dynamic Control Parameter. In: Proceedings of 3rd. IEEE International Conf. on Evolutionary Computation (1996)
Lis, J., Lis, M.: Self-adapting Parallel Genetic Algorithm with the Dynamic Mutation Probability, Crossover Rate and Population Size. In: Proceedings of 1st. Polish Nat. Conf. Evolutionary Computation (1996)
Lobo, F., Lima, C., Mártires, H.: An architecture for massive parallelization of the compact genetic algorithm. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 412–413. Springer, Heidelberg (2004)
Nuñez, A., Riff, M.-C.: Evaluating Migration Strategies for a graph-based evolutionary algorithm for CSP. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 196–204. Springer, Heidelberg (2000)
Pettinger, J., Everson, R.: Controlling Genetic Algorithms with Reinforcement Learning. In: Proceedings of the GECCO 2002 (2002 )
Riff, M.-C., Bonnaire, X.: Inheriting Parents Operators: A New Dynamic Strategy to improve Evolutionary Algorithms. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS (LNAI), vol. 2366, pp. 333–341. Springer, Heidelberg (2002)
Smith, J., Fogarty, T.C.: Operator and parameter adaptation in genetic algorithms. Soft Computing 1(2), 81–87 (1997)
Tongchim, S., Chongstitvatana, P.: Parallel genetic algorithm with parameter adaptation. Information Processing Letters 82(1), 47–54 (2002)
Tuson, A., Ross, P.: Adapting Operator Settings in Genetic Algorithms. Evolutionary Computation 2(6), 161–184 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bonnaire, X., Riff, MC. (2005). Using Self-Adaptable Probes for Dynamic Parameter Control of Parallel Evolutionary Algorithms. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_9
Download citation
DOI: https://doi.org/10.1007/11425274_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25878-0
Online ISBN: 978-3-540-31949-8
eBook Packages: Computer ScienceComputer Science (R0)