A comparative study of different variants of genetic algorithms for constrained optimization

Authors:
Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam
Affiliations:
School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, Australia;School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, Australia;School of Engineering and Information Technology, University of New South Wales at ADFA, Canberra, Australia
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

Over the last few decades, many different variants of Genetic Algorithms (GAs) have been introduced for solving Constrained Optimization Problems (COPs). However, a comparative study of their performances is rare. In this paper, our objective is to analyze different variants of GA and compare their performances by solving the 36 CEC benchmark problems by using, a new scoring scheme introduced in this paper and, a nonparametric test procedure. The insights gain in this study will help researchers and practitioners to decide which variant to use for their problems.