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

Abstract

This is the post-print version of the article. The official published version can be obtained from the link below - Copyright @ 2007 SpringerIn 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.This work was supported by FAPESP (Proc. 04/04289-6)

Self-adaptation of mutation distribution in evolution strategies for dynamic optimization problems

“…In the future, other control methods for the q parameter should be investigated, including self-organization [14]. Also, the proposed algorithm will be investigated on other DOPs.…”

Genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks

“…Although the restart scheme really works for some cases [13], for many DOPs it is more efficient to develop other approaches that make use of knowledge gathered from old environments. One of the possible approaches is to maintain and re-introduce diversity during the run of EAs, i.e., the immigrants schemes [14], [10], [17]. The random immigrants scheme is the simplest one where randomly generated new individuals are introduced into the population.…”

Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks

“…Though the restart scheme really works for some cases [8], for many DOPs it is more efficient to develop other approaches that make use of knowledge gathered from old environments. One of the possible approaches is to maintain and reintroduce diversity during the run of EAs, i.e., the immigrants schemes [9], [10], [11]. Elitism-based immigrants scheme [12] is a representative one among immigrants schemes for EAs in dynamic environments.…”