Tuson A, Ross P
Department of Artificial Intelligence, University of Edinburgh, U.K.
Evol Comput. 1998 Summer;6(2):161-84. doi: 10.1162/evco.1998.6.2.161.
In the majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has been argued that these settings should vary over the course of a genetic algorithm run--so as to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an investigation into this question. The effect upon genetic algorithm performance of two adaptation methods upon both well-studied theoretical problems and a hard problem from operations research, the flowshop sequencing problem, are therefore examined. The results obtained indicate that the applicability of operator adaptation is dependent upon three basic assumptions being satisfied by the problem being tackled.
在大多数遗传算法实现中，算子设置在给定的一次运行过程中是固定的。然而，有人认为这些设置应该在遗传算法运行过程中发生变化，以便考虑算子产生适应性增强的子代的能力的变化。本文描述了对此问题的一项研究。因此，研究了两种自适应方法对遗传算法性能的影响，这两种方法应用于经过充分研究的理论问题以及运筹学中的一个难题——流水车间排序问题。所获得的结果表明，算子自适应的适用性取决于所处理的问题要满足三个基本假设。
