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Title: A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems
Authors: Wang, H
Wang, D
Yang, S
Keywords: Genetic algorithm;Memetic algorithm;Local search;Crossover-based hill climbing;Mutation-based hill climbing;Dual mapping;Triggered random immigrants;Dynamic optimization problems
Issue Date: 2009
Publisher: Springer
Citation: Soft Computing, 13(8-9): 763 - 780, 2009
Abstract: Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.
Description: Copyright @ Springer-Verlag 2008
ISSN: 1432-7643
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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