Noise, sampling, and efficient genetic algorthms
Brad L. Miller · 1997
As genetic algorithms (GA) move into industry, a thorough understanding of how GAs are affected by noise is becoming increasingly important. Noise affects a GA's population sizing requirements, performance characteristics, and computational requirements. This research develops quantitative models for determining the effects of noise on the operation of a GA. Furthermore, the question of how to best optimize the performance of a GA in a noisy environments is investigated. Sampling fitness functions are explored, and techniques for determining the optimal sample size that maximizes the performance of a GA within a fixed computational time bound are presented.