Genetic Programming and Evolvable Machines

, Volume 8, Issue 3, pp 255–286

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

Original Paper

DOI: 10.1007/s10710-007-9024-z

Cite this article as:
Tinós, R. & Yang, S. Genet Program Evolvable Mach (2007) 8: 255. doi:10.1007/s10710-007-9024-z

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.

Keywords

Genetic algorithms Self-organized criticality Dynamic optimization problems Random immigrants 

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Departamento de Física e Matemática, FFCLRPUniversidade de São Paulo (USP)Ribeirão PretoBrazil
  2. 2.Department of Computer ScienceUniversity of LeicesterLeicesterUK