遗传算法的改进与应用

遗传算法是一种借鉴生物界自然选择和自然遗传机制的随机搜索算法,它与传统的算法不同。大多数古典的优化算法是基于一个单一的度量函数(评估函数)的梯度或较高次统计,以产生一个确定性的试验解序列;遗传算法不依赖于梯度信息,而是通过模拟自然进化过程来搜索最优解。该文针对传统遗传算法的缺陷,提出了一些新的改进思路,即从搜索技术和遗传算子等的角度来改进遗传算法。

Genetic Algorithm and Application

The genetic algorithm is a kind of natural selection from biological and natural genetic mechanisms of random search algorithm, it is different from the traditional method.Most of the classical algorithm is based on a single measurement function(evaluation function) or the gradient times higher statistics, in order to produce a definitive solution test sequence;genetic algorithm does not rely on gradient information, but through the simulation of natural evolution To search for optimal solutions.In this paper, the shortcomings of traditional genetic algorithm, put forward some new ideas to improve, that is, from the search technology and genetic operators such as the point of view to improve the genetic algorithm.