基于混合选择和动态变异的基因表达式编程

基因表达式编程（GEP）是一种基于基因型和表现型的新型遗传算法，目前被广泛应用在函数发现、时间序列预测和分类等领域。传统GEP算法采用轮盘赌方式来选择种群个体，其择优强度过大，易导致个体多样性减弱，产生“近亲繁殖”；种群个体的变异概率固定，变异幅度不能动态地适应每代的进化结果，影响进化效率。针对上述两个缺陷，本文对传统GEP做出两点改进：作者采用混合选择策略，以维持进化过程中个体的多样性，避免“近亲繁殖”；引入动态变异思想，使种群在进化过程中能根据自身适应性的高低来动态调整个体的变异概率，以最大限度地保留高适应度基因片段，消除低适应度基因片段。通过实验，本文验证了两项改进的有效性。

Gene Expression Programming Based on Mixed Selection and Dynamic Variation

Gene expression programming(GEP), which is a new type genetic algorithm based on genotype and phenotype, is now widely applied in function finding, time sequence prediction and classification.However, due to strong intensity in preferred roulette selection of individuals, traditional GEP often leads to reduction in the diversity of individuals and causes inbreeding;on the other hand, traditional GEP can not adapt to the evolution result of each generation by adjusting variation probability, so that the perf...