Application of Gene Expression Programming to Real Parameter Optimization

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.