Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9636
Title: Genetic algorithms with self-organized criticality for dynamic optimization problems
Authors: Tinos, R
Yang, S
Keywords: Computer science;Degradation;Environmental economics;Evolutionary computation;Genetic algorithms;Geology
Issue Date: 2005
Publisher: IEEE
Citation: IEEE, 3 pp. 2816 - 2823, 2005
Abstract: This paper proposes a genetic algorithm (GA) with random immigrants for dynamic optimization problems where the worst individual and its neighbours are replaced every generation. In this GA, the individuals interact with each other and, when their fitness is close, as in the case where the diversity level is low, one single replacement can affect a large number of individuals. This simple approach can take the system to a kind of self-organization behavior, known as self-organized criticality (SOC), which is useful to maintain the diversity of the population in dynamic environments and hence allows the GA to escape from local optima when the problem changes. The experimental results show that the proposed GA presents the phenomenon of SOC.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1555048
http://bura.brunel.ac.uk/handle/2438/9636
DOI: http://dx.doi.org/10.1109/CEC.2005.1555048
ISBN: 0-7803-9363-5
Appears in Collections:Dept of Computer Science Research Papers

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