期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2016
卷号：5
期号：1
页码：0090-0094
出版社：Shri Pannalal Research Institute of Technolgy
摘要：In this paper, Benchmark functions for single-objective optimization cases are presented and their performance is optimized using an optimization technique called Genetic algorithm. Genetic algorithm was introduced by John Holland at University of Michigan, United State in 1970s. It is a population and nature-inspired algorithm which selects the chromosomes of better fitness from the current population using Roulette wheel and removed the worst one. Selected chromosomes are used to produce the chromosomes of the best fitness for the next generation by applying genetic operators called offspring. After successive generations, the population moved towards an optimal solution and does not stuck out to local optima. Simulation results are shown by taking various Benchmark Functions namely: Sphere, Ackley, Booth, Easom and Matyas and their results are compared on parameters: population size, number of generations and dimensions.
