A Novel Evolutionary Algorithm Based on Multi-parent Crossover and Space Transformation Search

Authors:
Jing Wang;Zhijian Wu;Hui Wang;Lishan Kang
Affiliations:
State Key Lab of Software Engineering, Wuhan University, Wuhan, China 430072 and Software College, Jiangxi University of Finance and Economics, Nanchang, China 330013;State Key Lab of Software Engineering, Wuhan University, Wuhan, China 430072;State Key Lab of Software Engineering, Wuhan University, Wuhan, China 430072;State Key Lab of Software Engineering, Wuhan University, Wuhan, China 430072
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

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Abstract

This paper presents a novel hybrid evolutionary algorithm for function optimization. In this algorithm, the space transformation search (STS) is embedded into a novel genetic algorithm (GA) which employs a novel crossover operator based on a nonconvex linear combination of multiple parents and elite-preservation strategy (EGT). STS transforms the search space to increase more opportunities for finding the global optimum and accelerate convergence speed. Experimental studies on 15 benchmark functions show that the STS-EGT not only has good ability to help EGT jump out of local optimum but also obtains faster convergence than the STS-GT which has no elitepreservation strategy.