Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/1117
Title: A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Authors: Tinos, Renato
Yang, Shengxiang
First Published: Sep-2007
Publisher: Springer
Citation: Genetic Programming and Evolvable Machines, 2007, 8(3), pp.255-286
Abstract: In this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organizing behavior, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to the dynamics.
Links: http://hdl.handle.net/2381/1117
Type: Article
Description: This is the authors' final draft of the paper published as Genetic Programming and Evolvable Machines, 2007, 8(3), pp. 255-286. The original publication is available at www.springerlink.com, DOI: 10.1007/s10710-007-9024-z
Appears in Collections:Published Articles, Dept. of Computer Science

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