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2008 Fourth International Conference on Natural Computation (2008)
Oct. 18, 2008 to Oct. 20, 2008
ISBN: 978-0-7695-3304-9
pp: 273-277
ABSTRACT
Gene Expression Programming (GEP) is a new evolutionary algorithm that implements genome/phoneme representations. Despite its powerful global search ability and wide application in symbolic regression, little work has been done to apply it to real parameter optimization. A real parameter optimization method named Uniform-Constant based GEP (UC-GEP) is proposed in this paper. The main work and contributions include: (1) Compares UC-GEP with Meta-Constant based GEP (MC-GEP), Meta-Uniform-Constant based GEP (MUC-GEP), and Floating Point Genetic Algorithm (FP-GA) on optimizing seven benchmark functions, respectively. Experiment results show that GEP methods outperform FP-GA on five of the seven functions and UC-GEP reaches the global optimum on all seven functions. (2) Compares UC-GEP with both MC-GEP and MUC-GEP on optimizing Rastrigin and Griewangk with various dimensions. Experiment results also show that UC-GEP is the best among these three algorithms.
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CITATION

C. Tang, Y. Liu, K. Xu, J. Zhu, R. Tang and J. Zuo, "Application of Gene Expression Programming to Real Parameter Optimization," 2008 Fourth International Conference on Natural Computation(ICNC), vol. 06, no. , pp. 273-277, 2008.
doi:10.1109/ICNC.2008.511
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