A novel method for real parameter optimization based on Gene Expression Programming

Authors:
Kaikuo Xu;Yintian Liu;Rong Tang;Jie Zuo;Jun Zhu;Changjie Tang
Affiliations:
School of Computer Science, Sichuan University, Chengdu 610065, China;Chengdu University of Information Technology, Chengdu 610225, China;Chengdu University of Information Technology, Chengdu 610225, China and School of Computer Science, Sichuan University, Chengdu 610065, China;School of Computer Science, Sichuan University, Chengdu 610065, China;National Center for Birth Defects Monitoring, Chengdu 610041, China;School of Computer Science, Sichuan University, Chengdu 610065, China
Venue:
Applied Soft Computing
Year:
2009

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Abstract

Gene Expression Programming (GEP) is a new technique of evolutionary algorithm that implements genome/phoneme representation in computing programs. Due to its power in global search, it is widely applied in symbolic regression. However, little work has been done to apply it to real parameter optimization yet. This paper proposes a real parameter optimization method named Uniform-Constants based GEP (UC-GEP). In UC-GEP, the constant domain directly participates in the evolution. Our research conducted extensive experiments over nine benchmark functions from the IEEE Congress on Evolutionary Computation 2005 and compared the results to three other algorithms namely Meta-Constants based GEP (MC-GEP), Meta-Uniform-Constants based GEP (MUC-GEP), and the Floating Point Genetic Algorithm (FP-GA). For simplicity, all GEP methods adopt a one-tier index gene structure. The results demonstrate the optimal performance of our UC-GEP in solving multimodal problems and show that at least one GEP method outperforms FP-GA on all test functions with higher computational complexity.