一种求解参数优化问题的引导交叉算子

提出一种应用于参数优化问题的引导交叉算子。该交叉算子利用父代染色体的适应值差异，引导交叉操作产生的子代向适应值高的父代倾斜，以产生高适应值的子代个体。对于连续函数，高适应值个体的邻域内也是高适应值的个体，且在两个个体之间不存在极值时，朝适应值增加的方向可以生成更优的个体。实验表明，对比常用的算术交叉算子，引导交叉算子具有更强的全局、局部搜索能力和更快的搜索速度。

Guided Crossover for Parameter Optimization

A kind of guided crossover for the parameter optimization problem is proposed. The fitness difference between parents would be used for guiding the generation of tbe offspring so that the Euclidean distance between offspring and better parent will be smaller. There are two reasons for this strategy which can achieve better performance than random crossover. For the continuous functions, firstly, the neighborhood of higher fitness individuals has better individuals, and better offspring can be generated toward the improved direction of fitness. Case studies of the numerical simulations are given to demonstrate that guided crossover has higher efficiency and better ability of global and local search, as compared with conventional arithmetical crossover.