Search space division in GAs using phenotypic properties

Authors:
Shigeyoshi Tsutsui;Ashish Ghosh
Affiliations:
Department of Management and Information Science, Hannan University, 5-4-3 Amamihigashi, Matsubara, Osaka 580, Japan;Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Calcutta 700035, India
Venue:
Information Sciences: an International Journal
Year:
1998

Citing 9
Cited 1

Global optimization

Global optimization
Dynamic Parameter Encoding for Genetic Algorithms

Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization

Proceedings of the 3rd International Conference on Genetic Algorithms
A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization

Proceedings of the 3rd International Conference on Genetic Algorithms
Forking Genetic Algorithm with Blocking and Shrinking Modes (fGA)

Proceedings of the 5th International Conference on Genetic Algorithms
A sequential niche technique for multimodal function optimization

Evolutionary Computation
Changing representations during search: A comparative study of delta coding

Evolutionary Computation
Forking genetic algorithms: Gas with search space division schemes

Evolutionary Computation

A variable-grouping based genetic algorithm for large-scale integer programming

Information Sciences: an International Journal

Quantified Score

Hi-index	0.07

Visualization

Abstract

In this article, we study a new type of forking GA (fGA), the phenotypic forking GA (p-fGA). The fGA divides the whole search space into subspaces depending on the convergence status of the population and the solutions obtained so far; and is intended to deal with multimodal problems which are difficult to solve using conventional GA. We use a multi-population scheme, which includes one parent population that explores one subspace, and one or more child population(s) exploiting the other subspace. The p-fGA divides the search space using phenotypic properties only, and defines a search subspace (to be exploited by a child population) by a neighborhood hypercube around the current best individual in the phenotypic feature space. Empirical results on complex function optimization problems show that the p-fGA performs fairly well compared to a conventional GA. Two other variants of the p-fGA, the moving window p-fGA (to accelerate the speed of convergence in the child populations) and the variable resolution p-fGA (to solve multimodal problems with high precision) are also studied in this article.