Structure of Multi-Stage Composite Genetic Algorithm (MSC-GA) and its performance

Authors:
Fachao Li;Li Da Xu;Chenxia Jin;Hong Wang
Affiliations:
School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China;Department of Information Technology and Decision Science, Old Dominion University, Norfolk, VA 23529, USA and Institute of Systems Science and Engineering, Wuhan University of Technology, Wuhan 4 ...;School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China;School of Business and Economics, North Carolina A&T State University, Greensboro, NC 27411, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In view of the slowness and the locality of convergence for Simple Genetic Algorithm (SGA) in solving complex optimization problems, we propose an improved genetic algorithm named Multi-Stage Composite Genetic Algorithm (MSC-GA) through reducing the optimization-search range gradually, and the structure and implementation steps of MSC-GA is also discussed. Then, we consider its global convergence under the elitist preserving strategy using the Markov chain theory and analyze its performance through three examples from different aspects. The results indicate that the new algorithm possesses several advantages such as better convergence and less chance of being trapped into premature states. As a result, it can be widely applied to many large-scale optimization problems which require higher accuracy.