Hybridizing Genetic Algorithm with Cross Entropy for Solving Continuous Functions
Authors: 
Pedro Lopez-Garcia, Enrique Onieva, Eneko Osaba, Antonio D. Masegosa, Asier PerallosAuthors Info & Claims
Pedro Lopez-Garcia, Enrique Onieva, Eneko Osaba, Antonio D. Masegosa, Asier PerallosAuthors Info & ClaimsPages 763 - 764
Abstract
In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions with different characteristics, using different configurations.
References
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Holtschulte, N.J., Moses, M.: Benchmarking cellular genetic algorithms on the bbob noiseless testbed. Proceeding of the 15th GECCO conference pp. 1201--1208 (2013)
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Hutter, F., Hoos, H., Leyton-Brown, K.: An evaluation of sequential model-based optimization for expensive blackbox functions. Proceeding of the 15th GECCO conference pp. 1209--1216 (2013)
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- Hybridizing Genetic Algorithm with Cross Entropy for Solving Continuous Functions
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July 2015
1568 pages
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Association for Computing Machinery
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Publication History
Published: 11 July 2015
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- European Commission
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GECCO '15
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