A Study of Genetic Algorithm and Crossover Techniques

Genetic algorithms are inspired by Darwin's theory of natural evolution. In the natural world, organisms who are poorly suited for the environment die off, while those well suited, prosper. Genetic algorithms search the space of individuals for good candidates. The chance of particular individual being selected is proportional to the amount by which its fitness is greater or less than its competitors' fitness. Genetic algorithms are ways of solving problems by mimicking processes nature uses; i.e. Selection, Crossover, Mutation and accepting, to evolve a solution to a problem. Many crossover techniques exist for organism which uses different data structures to store themselves. Genetic algorithm which is one of the most well-known heuristic approaches, crossover components and crossover techniques, which are the most important property of the Genetic algorithms performance, has been discussed.