Author(s)

H. Wan

Abstract

The application of the genetic algorithm to several problems in optimization is described. The performances in each of these experiments were compared with those of conventional methods. The pros and cons of using the genetic algorithm in the examples are also discussed. INTRODUCTION Since the genetic algorithm (GA) was first proposed by Holland**-, it has not been receiving too much attention for decades until the late 80s. It occurred at a time when connectionists were desperate for finding some quick and easy ways to determine the weights on neural networks (Ackley^, Koza et al.^). Having been accepted by AI researchers, its effectiveness and resemblance to biological evolution is gaining more and more admiration and more and more research areas in which GA is applicable are being discovered. The topic of optimization is at

Keywords