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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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Associative memory scheme for genetic algorithms in dynamic environments
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Proceedings of the 14th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
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GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
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Applied Computational Intelligence and Soft Computing
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In recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications. Several approaches have been developed, such as the memory scheme. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic optimization problems. A PBIL-specific memory scheme is proposed to improve its adaptability in dynamic environments. In this memory scheme the working probability vector is stored together with the best sample it creates in the memory and is used to reactivate old environments when change occurs. Experimental study based on a series of dynamic environments shows the efficiency of the memory scheme for PBILs in dynamic environments. In this paper, the relationship between the memory scheme and the multi-population scheme for PBILs in dynamic environments is also investigated. The experimental results indicate a negative interaction of the multi-population scheme on the memory scheme for PBILs in the dynamic test environments.