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Title: Memory-based immigrants for genetic algorithms in dynamic environments
Authors: Yang, S
Keywords: Genetic algorithms;Dynamic optimization problems;Memory scheme;Random immigrants scheme
Issue Date: 2005
Publisher: ACM
Citation: Genetic and Evolutionary Computation Conference (GECCO 2005), Washington DC, USA, 2: 1115 - 1122, 25 - 29 Jun 2005
Abstract: Investigating and enhancing the performance of genetic algorithms in dynamic environments have attracted a growing interest from the community of genetic algorithms in recent years. This trend reflects the fact that many real world problems are actually dynamic, which poses serious challenge to traditional genetic algorithms. Several approaches have been developed into genetic algorithms for dynamic optimization problems. Among these approches, random immigrants and memory schemes have shown to be beneficial in many dynamic problems. This paper proposes a hybrid memory and random immigrants scheme for genetic algorithms in dynamic environments. In the hybrid scheme, the best solution in memory is retrieved and acts as the base to create random immigrants to replace the worst individuals in the population. In this way, not only can diversity be maintained but it is done more efficiently to adapt the genetic algorithm to the changing environment. The experimental results based on a series of systematically constructed dynamic problems show that the proposed memory based immigrants scheme efficiently improves the performance of genetic algorithms in dynamic environments.
Description: Copyright @ 2005 ACM
ISBN: 1-59593-010-8
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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