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The Strategies of Initial Diversity and Dynamic Mutation Rate for Gene Expression Programming

Gene expression programming (GEP) is a genotype/phenotype system that evolves candidate solutions encoded in linear chromosomes of fixed length. Its characteristics make GEP surpass other evolutionary algorithms. However, the original GEP algorithm generates the initial population in a simple way and uses a constant mutation rate determined empirically. Two effective strategies are proposed to overcome these limitations. The contributions of this paper include: (1) developing an algorithm to extract the Open Reading Frame of a gene without parsing the Expression Tree, (2) defining the consanguineous relation between chromosomes, (3) proposing an algorithm to diversify the initial population based on the consanguineous relation, and (4) proposing a novel adaptive mutation rate strategy for each chromosome in evolution. Furthermore, a performance evaluation using both synthetic and real-life data demonstrates that the proposed strategies are effective. The result shows that the correlation coefficient between observed values and predicted values can be increased by as high as 0.1856, and the classification problem can be solved in less running time.