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Enhancing evolutionary algorithms through recombination and parallelism
Keywords:
Evolutionary algorithms, multirecombination, parallel genetic algorithms, strategies for migration controlAbstract
Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.
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References
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[11] Esquivel S., Leiva H.,.Gallard R., Multiple Crossovers Between Multiple Parents To Improve Search In Evolutionary Algorithms. Proceedings of the 1999 Congress on Evolutionary Computation (IEEE). Washington DC, pp 1589-1594.
[12] Holland J. – Adaptation in Natural and Artificial Systems – Ann Arbor, MI: University of Michigan Press. 1975
[13] Levine, D. - A Parallel Genetic Algorithm for the Set Partitioning Problem - Ph D Thesis, Illinois Institute of Technology and Argone National Lab. (ANL -94/23), 1994.
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[15] Ochoa C., Gallard R. - Strategies for Migration Overseeing in Asynchronous Schemes of Parallel Genetic Algorithms - Proceedings of the International ICSC Symposium on Soft Computing: SOCO'97.
[16] Schaffer J. D., Some experiments in machine learning using vector evaluated genetic algorithms, Doctoral dissertation, Department of Electrical Engineering, Vanderbilt University. 1984.
[17] Shyh-Chang Lin, Punch W., Goodman E. - Coarse-Grain Parallel Genetic Algorithms: Categorizations and New Approach. Parallel & Distributed Processing, Dallas TX, Oct. 1994.
[18] Spears William M.: Adapting Crossover in Evolutionary Algorithms. Proceedings of the Evolutionary Programming Conference, 1995.
[19] Tanese R. - Distributed Genetic Algorithms - Proceedings of the Third International Conference on Genetic Algorithms, pp 434-439, Morgan Kauffman, 1989.
[20] Whitley D. - An Executable Model of a Simple Genetic Algorithm - Fundations of Genetic Algorithms-2 (FOGA-92), pp 45-62, Morgan Kaufmann, 1993.
[2] Belding T.C. - The Distributed Genetic Algorithm Revisited - Proceedings of the Sixth International Conference on Genetic Algorithms, pp 114-121, Morgan Kauffman, 1995.
[3] Cohoon J.P., Hedge S.U., Martin W.N., Richards D. - Puntuacted Equilibria: A Parallel Genetic Algorithm - Proceedings of the Second International Conference on Genetic Algorithms, pp 148-154, Lawrence Erlbaum Associates Publishers, 1987.
[4] Easom, E.: A Survey of Global Optimization techniques. M. Eng. Thesis, Univ. Louisville, Louisville, KY, 1990.
[5] Eiben A., Lis J., A multisexual genetic algorithm for multiobjective optimization, 4th IEEE International Conf. on Evolutionary Computation (ICEC'97), pp 59-64, Indianapolis, USA, April 1997.
[6] Esquivel S., Gallard R., Michalewicz Z., - MCPC: Another Approach to Crossover in Genetic Algorithms- Proceedings of the 1st Congreso Argentino de Cs. de la Computación, pp 141-150, Universidad Nacional del Sur, October 1995.
[7]. Esquivel S., Leiva A., Gallard R., - Multiple crossover per couple in genetic algorithms. Proc. of the 4th IEEE International Conf. on Evolutionary Computation (ICEC'97), pp 103-106, Indianapolis, USA, April 1997.
[8] Esquivel S., Leiva A., Gallard R.: Couple Fitness Based Selection with Multiple Crossover per Couple in Genetic Algorithms. Proceedings of the International Symposium on Engineering of Intelligent Systems (EIS ́98), Vol. 1, pp 235-241, La Laguna, Tenerife, Spain, February 1998.
[9] Esquivel S., Leiva H.,.Gallard R., Self-Adaptation of Parameters for MCPC in Genetic Algorithms, Proceedings of the 4th Congreso Argentino de Ciencias de la Computación (CACiC’98), pp 419-425. Universidad Nacional del Comahue, Argentina, October, 1998.
[10] Esquivel S., Leiva H.,.Gallard R., A Study of Alternative Selection Mechanisms for Multiple Crossover per Couple in Genetic Algorithms, Proceedings of the 4th Congreso Argentino de Ciencias de la Computación (CACiC’98), pp 383-391. Universidad Nacional del Comahue, Argentina, October 1998.
[11] Esquivel S., Leiva H.,.Gallard R., Multiple Crossovers Between Multiple Parents To Improve Search In Evolutionary Algorithms. Proceedings of the 1999 Congress on Evolutionary Computation (IEEE). Washington DC, pp 1589-1594.
[12] Holland J. – Adaptation in Natural and Artificial Systems – Ann Arbor, MI: University of Michigan Press. 1975
[13] Levine, D. - A Parallel Genetic Algorithm for the Set Partitioning Problem - Ph D Thesis, Illinois Institute of Technology and Argone National Lab. (ANL -94/23), 1994.
[14] Pareto V., Cours d’Economie Politique, 1896, Switzerland, Lausanne: Rouge.
[15] Ochoa C., Gallard R. - Strategies for Migration Overseeing in Asynchronous Schemes of Parallel Genetic Algorithms - Proceedings of the International ICSC Symposium on Soft Computing: SOCO'97.
[16] Schaffer J. D., Some experiments in machine learning using vector evaluated genetic algorithms, Doctoral dissertation, Department of Electrical Engineering, Vanderbilt University. 1984.
[17] Shyh-Chang Lin, Punch W., Goodman E. - Coarse-Grain Parallel Genetic Algorithms: Categorizations and New Approach. Parallel & Distributed Processing, Dallas TX, Oct. 1994.
[18] Spears William M.: Adapting Crossover in Evolutionary Algorithms. Proceedings of the Evolutionary Programming Conference, 1995.
[19] Tanese R. - Distributed Genetic Algorithms - Proceedings of the Third International Conference on Genetic Algorithms, pp 434-439, Morgan Kauffman, 1989.
[20] Whitley D. - An Executable Model of a Simple Genetic Algorithm - Fundations of Genetic Algorithms-2 (FOGA-92), pp 45-62, Morgan Kaufmann, 1993.
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Published
2001-10-01
How to Cite
Gallard, R. H., & Esquivel, S. C. (2001). Enhancing evolutionary algorithms through recombination and parallelism. Journal of Computer Science and Technology, 1(05), 13 p. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/985
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