文章基本信息

标题：Genetic Algorithms and Programming-An Evolutionary Methodology
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作者：T. Venkat Narayana Rao ; Srikanth Madiraju
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2010
卷号：1
期号：5
页码：427-437
出版社：TechScience Publications
摘要：Genetic Programming (GP) is an automated method for creating a working computer program from a high-level problem statement for a given problem. Genetic programming starts from a high-level statement of “what needs to be done” and to automatically create a computer program to solve the problem. In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user defined task. It is a specialization of genetic algorithms (GA) where in each individual is a computer program. It is a machine learning technique used to optimize population of computer programs according to a fitness span determined by a program's ability to perform a given computational task. This paper presents an idea pertaining to the principles of genetic programming which includes relative effectiveness of mutation, crossover, breeding computer programs and fitness test in genetic programming. The literature of traditional genetic algorithms contains related studies, but through GP, it saves time by freeing the human from having to design complex algorithms. GP not only help in designing the algorithms but it could assist in creating the optimal solutions than traditional counterparts in a noteworthy ways
关键词：Genetic Programming; subtree; chromosomes;mutation; Evolutionary.