This paper discusses the best approaches to take with Genetic algorithms (GA), a branch of evolutionary computing (EC) that mimics the theory of evolution and natural selection, where the technique is based on an heuristic random search. Crossover and mutation are the key to success in genetic algorithms. With the existence of several methods of crossover and mutation operators, this paper attempts to determine which method is best suited to each problem.
