Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5988
Title: Genetic algorithms with elitism-based immigrants for changing optimization problems
Authors: Yang, S
Keywords: Dynamic optimization problems;Genetic algorithms;Random immigrants;Elitism-based immigrants
Issue Date: 2007
Publisher: Springer
Citation: EvoWorkshops 2007: Applications of Evolutionary Computing, Lecture Notes in Computer Science 4448: 627 - 636, 2007
Abstract: Addressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into genetic algorithms to enhance their performance in dynamic environments. One major approach is to maintain the diversity of the population, e.g., via random immigrants. This paper proposes an elitism-based immigrants scheme for genetic algorithms in dynamic environments. In the scheme, the elite from previous generation is used as the base to create immigrants via mutation to replace the worst individuals in the current population. This way, the introduced immigrants are more adapted to the changing environment. This paper also proposes a hybrid scheme that combines the elitism-based immigrants scheme with traditional random immigrants scheme to deal with significant changes. The experimental results show that the proposed elitism-based and hybrid immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.
Description: Copyright @ Springer-Verlag Berlin Heidelberg 2007.
URI: http://www.springerlink.com/content/q217517757p1975w/
http://bura.brunel.ac.uk/handle/2438/5988
DOI: http://dx.doi.org/10.1007/978-3-540-71805-5_69
ISSN: 0302-9743
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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