Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5804
Title: Genetic algorithms with memory- and elitism-based immigrants in dynamic environments
Authors: Yang, S
Keywords: Genetic algorithms;Dynamic optimization problems;Memory;Random immigrants;Memory-based immigrants;Elitism-based immigrants
Issue Date: 2008
Publisher: Massachusetts Institute of Technology
Citation: Evolutionary Computation, 16(3): 385 - 416, Sep 2008
Abstract: In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.
Description: Copyright @ 2008 by the Massachusetts Institute of Technology
URI: http://bura.brunel.ac.uk/handle/2438/5804
DOI: http://dx.doi.org/10.1162/evco.2008.16.3.385
ISSN: 1063-6560
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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