基于可进化性的自适应遗传算法

针对传统遗传算法容易陷入局部最优解的问题，提出一个基于可进化性的自适应遗传算法。将个体可进化性作为适应度函数的参数加入到随进化代数动态调整的非线性适应度函数中，动态调整整个种群的交叉与变异概率以逸出局部最优。实验结果表明，该算法可改善适应度不高但具有较好进化能力个体的生存概率，且提高了种群多样性与搜索效率。

Self-adaptive Genetic Algorithm Based on Evolvability

Aiming at the problem of traditional genetic algorithm is easy to involve in local optima, this paper presents a self-adaptive genetic algorithm based on evolvability. The individual evolability as a parameter is put into the nonlinear fitness function which dynamically adjustment with the evolution algebra, and it adjusts dynamically the crossover and mutation probability to runaway the local optima. Experimental results show that this algorithm can improve the survival probability of the individuals with better evolvability but worse fitness, and enhances population diversity and search efficiency.