Design of an Adaptive Gene Expression Programming Algorithms Based on Data Analysis
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Gene expression programming is a new adaptive evolutionary algorithm based on the structure and function of biological genes. This algorithm has the shortcomings of slow convergence speed, easy to fall into local optimum and low fitting degree when solving specific problems. Based on data analysis, this paper proposes an adaptive gene expression program algorithm, which can adaptively adjust the crossover and mutation probability of the algorithm, thus effectively avoiding the sensitivity of artificial setting of initial parameters. It applies differential mutation search, chaotic reorganization and mutation operation, catastrophe operator to GEP. The results show that the algorithm not only improves the accuracy and convergence speed of the algorithm, but also effectively overcomes the immature convergence. The theory proves the global convergence of the algorithm. The improved genetic expression program has good performance.
Gene Expression Programming, Data Analysis, Convergence