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A self-organizing random immigrants genetic algorithm for dynamic optimization problems

By Renato Tinos and Shengxiang Yang


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, DOI: 10.1007/s10710-007-9024-zIn this paper a genetic algorithm is proposed where the worst individual and\ud individuals with indices close to its index are replaced in every generation by randomly\ud 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\ud the dynamics

Publisher: Springer
Year: 2007
DOI identifier: 10.1007/s10710-007-9024-z
OAI identifier:

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