Genetic algorithms with self-organizing behaviour in dynamic environments

Copyright @ 2007 Springer-VerlagIn recent years, researchers from the genetic algorithm (GA) community have developed several approaches to enhance the performance of traditional GAs for dynamic optimization problems (DOPs). Among these approaches, one technique is to maintain the diversity of the population by inserting random immigrants into the population. This chapter investigates a self-organizing random immigrants scheme for GAs to address DOPs, where the worst individual and its next neighbours are replaced by random immigrants. In order to protect the newly introduced immigrants from being replaced by fitter individuals, they are placed in a subpopulation. In this way, individuals start to interact between themselves and, when the fitness of the individuals are close, one single replacement of an individual can affect a large number of individuals of the population in a chain reaction. The individuals in a subpopulation are not allowed to be replaced by individuals of the main population during the current chain reaction. The number of individuals in the subpopulation is given by the number of individuals created in the current chain reaction. It is important to observe that this simple approach can take the system to a self-organization behaviour, which can be useful for GAs in dynamic environments.Financial support was obtained from FAPESP (Proc. 04/04289-6)

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

Copyright @ IOS Press. All Rights Reserved.Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator

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

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)

An analysis of the XOR dynamic problem generator based on the dynamical system

This is the post-print version of the article - Copyright @ 2010 Springer-VerlagIn this paper, we use the exact model (or dynamical system approach) to describe the standard evolutionary algorithm (EA) as a discrete dynamical system for dynamic optimization problems (DOPs). Based on this dynamical system model, we analyse the properties of the XOR DOP Generator, which has been widely used by researchers to create DOPs from any binary encoded problem. DOPs generated by this generator are described as DOPs with permutation, where the fitness vector is changed according to a permutation matrix. Some properties of DOPs with permutation are analyzed, which allows explaining some behaviors observed in experimental results. The analysis of the properties of problems created by the XOR DOP Generator is important to understand the results obtained in experiments with this generator and to analyze the similarity of such problems to real world DOPs.This work was supported by Brazil FAPESP under Grant 04/04289-6 and by UK EPSRC under Grant EP/E060722/2

Multi-population methods with adaptive mutation for multi-modal optimization problems

open access journalThis paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms

Constructing dynamic test environments for genetic algorithms based on problem difficulty

This article is posted here with permission from IEEE - Copyright @ 2004 IEEEIn recent years the study of dynamic optimization problems has attracted an increasing interest from the community of genetic algorithms and researchers have developed a variety of approaches into genetic algorithms to solve these problems. In order to compare their performance, an important issue is the construction of standardized dynamic test environments. Based on the concept of problem difficulty, This work proposes a new dynamic environment generator using a decomposable trap function. With this generator, it is possible to systematically construct dynamic environments with changing and bounding difficulty and hence, we can test different genetic algorithms under dynamic environments with changing but controllable difficulty levels.This research was supported by UK EPSRC under Grant GR/S79718/01