基于协同进化遗传算法的模型拟合研究

普通遗传进化算法在解决模型拟合问题中，建模与优化顺序结构时优化效果有限、拟合速度慢、稳定性低。针对上述问题，提出基于协同进化遗传算法的模型拟合算法。该算法将建模与优化问题抽象成多种群间协同进化，通过种群间整体的适应度值交换，将种群关联起来，扩大智能算法建模过程中参数优化的时空作用范围。各种群间含有不同基因表达，在解决局部问题时具有自包含性，有利于更好地发挥各智能算法(遗传算法、遗传规划)的优势。实验结果表明，该算法的稳定性和收敛速度优于传统遗传进化算法。

Research of Model Fitting Based on Co-evolutionary Genetic Algorithm

This paper proposes an advanced co-evolutionary model fitting algorithm. It optimizes the process in the course of solving the symbolic regression, especially to the shortcomings of traditional Genetic Algorithm(GA). It abstracts the modeling and optimization into a variety of inter-group co-evolution, associating these populations through exchange of fitness value, while extending the intelligent algorithm both in spatial and temporal scope when optimizing the parameters modeling. For the various groups with different gene expression, they have their nature self-contained in solving certain problems. It is more conducive to take advantages of the intelligent algorithms(GA, Genetic Programming(GP)). Compared with the traditional algorithm, the co-evolutionary model fitting algorithm shows a significant improvement in stability and convergence rate.