用归纳学习引导进化

遗传算法需要在选择操作的正向作用和交叉／变异的破坏性影响之间取得平衡，否则往往会出现收敛速度慢或未成熟收敛，不同于现有的其它改进，论文采用归纳学习的方法来引进化过程趋于这一平衡，文中采用归纳学习的方法，从进化历史进程中抽取出能反映过去进化的错误和成功的规则，进而用它们来引导后读的进化过程，保证在避免重复错误的同时加速进化，基于函数优化和布局求解的实验验证了该方法的有效性。

Conduct Evolution Using Induction Learning

Genetic algorithms must try to keep the balance between the positive effect of the selector and the disruptiveness of crossover or mutation, otherwise it will suffer from premature convergence or low convergence speed. Unlike other improvement works, this paper proposes a method that conducts evolution to keep the balance through induction learning. Rules were drawn from previous evolutions which reflect past successes or failures. They were then used to conduct evolutions to avoid the repeated failures and speed up the evolution. Experiments on function optimization and facility layout have validated its effectiveness.