遗传算法机理的研究

众所周知,“模式定理”和“隐性并行性”是遗传算法(genetic algorithms,简称GA算法)的两大理论基础.该文对这两个原理进行分析,指出这两个原理存在有不严格和不足之处,即作为GA算法的基础,这两个原理尚欠完善.为加深对GA的理解，文章提出遗传算法的一个新的改进模型——理想浓度模型.通过对此模型的分析,得出遗传算法本质上是一个具有定向制导的随机搜索技术.其定向制导原则是,导向以适应度高的模式为祖先的染色体“家族”方向.最后给出两个典型的函数求最大值的模拟例子.从模拟结果看,改进后的GA算法大大提高了算法的速度,解的精度也有所提高.这说明新算法具有应用的潜力.

Research on the Mechanism of Genetic Algorithms

It's well known that the schemata theorem and the implicit parallelism are two basic theoretical foundations of genetic algorithms (GA). In this paper, the authors analyze the two basic principles and show that the two principles are not strict and have some disadvantages. That is, as the bases of GAs, the theorems are not perfect. In order to deepen the comprehension of GA, a new ideal density model of GA is presented in this paper. Based on the model, it's known that the GA is actually a guiding stochastic search. And the searching direction is guided onto the chromosome family whose ancestors belong to schemata with high fitness. Using the model to solve the typical function optimization problem, the simulation results show that the new GA has much better speed and can get more precise results. This shows that the new GA model has potential usage in practice.