Study on Genetic Algorithm and Implementation of Various Test Function using Crossover Operator
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 5
Abstract
Nature has always been a great source of inspiration to all mankind. Genetic Algorithms(GAs) are search based algorithms based on the concepts of natural selection and genetics. GAs is a subset of a much larger branch of computation known as Evolutionary Computation. There are a number of operators available for each and every phase. Crossover is one such phase for which a variety of crossover operators exist. It becomes very confusing to select one crossover operator. Therefore, a comparative study of various crossover operators is provided to help researchers select any one of the available crossover operators. The study includes a deep study of various aspects of genetic algorithms and its phases. In this we can randomly create initial population and assign fitness value it depends on selection criteria . Then we create new offspring and calculate the domain value to find out the best operator.
Authors and Affiliations
Avni Sharma, Sanjay
Keywords
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