改进的多表达式编程算法在函数发现中的应用

Application of improved multi expression programming algorithm in function finding problem

为了提高多表达式编程算法的效率,研究了MEP基本算法的适应度函数、杂交策略和变异策略,对其进行了优化,提出了一种改进的MEP算法。改进的MEP算法采用归一化的均方根差作为适应度函数,采用概率区间来选择杂交算子,并使变异概率在进化过程中随着进化代数和适应值的变化进行动态调整。最后将改进的MEP算法应用于函数发现问题中,对算法的效率进行了验证。实验结果表明,与MEP基本算法相比,改进的MEP算法能够更快速地找到目标函数,算法的效率得到了提高。

To improve the efficiency ofmulti expression programming （MEP） algorithm, the fitness function, crossover strategy and mutation strategy of the basic MEP algorithm is studied and optimized, and an improved algorithm is proposed. The normalized root mean square error （RMSE） is adopted in the improved MEP algorithm. Probability interval is used to choose crossover operators, and mutation probability is dynamically adjusted with evolutionary generation and fitness value in evolution process. At last, the improved MEP algorithm is applied to solve function finding problem, and efficiency of the algorithm is verified. The experimental results show that the improved MEP algorithm can find the target function more quickly than the basic MEP algorithm, which proves the efficiency of the algorithm is enhanced.