Paper Info
Title
Structure of Multi-Stage Composite Genetic Algorithm (MSC-GA) and its performance
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.
Year
DOI
Venue
2011
10.1016/j.eswa.2011.01.110
Expert Syst. Appl.
Keywords
Field
DocType
complex optimization problem,multi-stage composite genetic algorithm,multi-stage composite genetic algorithm (msc-ga),better convergence,new algorithm,different aspect,large-scale optimization problem,genetic algorithm,markov chain theory,convergence,simple genetic algorithm,global convergence,markov chain,optimization,improved genetic algorithm,optimization problem
Computer science,FSA-Red Algorithm,Artificial intelligence,Population-based incremental learning,Optimization problem,Genetic algorithm,Mathematical optimization,Meta-optimization,Markov chain,Algorithm,Genetic representation,Cultural algorithm,Machine learning
Journal
Volume
Issue
ISSN
38
7
Expert Systems With Applications
Citations 
PageRank 
References 
18
0.76
19
Authors
4
Name
Order
Citations
PageRank
Fachao Li115722.30
Lida Xu26275279.34
Chenxia Jin310113.20
Hong Wang41357.79