期刊名称：Oriental Journal of Computer Science and Technology
印刷版ISSN：0974-6471
出版年度：2008
卷号：1
期号：2
页码：155-166
语种：English
出版社：Oriental Scientific Publishing Company
摘要：Genetic Algorithms (GA’s) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GA’s is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem. Genetic Algorithms has been widely studied, experimented and applied in many fields in engineering worlds. Not only does GA’s provide alternative methods to solving problem, it consistently outperforms other traditional methods in most of the problems link. Many of the real world problems involved finding optimal parameters, which might prove difficult for traditional methods but ideal for GA’s. However, because of its outstanding performance in optimization, GA’s has been wrongly regarded as a function optimizer. In fact, there are many ways to view genetic algorithms. GA’s as problem solvers GA’s as challenging technical puzzle GA’s as basis for competent machine learning GA’s as computational model of innovation and creativity GA’s as computational model of other innovating systems GA’s as guiding philosophy However, due to various constraints, we would only be looking at GA’s as problem solvers and competent machine learning here. We would also examine how GA’s is applied to completely different fields. Many scientists have tried to create living programs. These programs do not merely simulate life but try to exhibit the behaviors and characteristics of real organisms in an attempt to exist as a form of life.
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