Improvements in genetic algorithms

This paper presents an exhaustive study of the Simple Genetic Algorithm (SG A), Steady State Genetic Algorithm (SSGA) and Replacement Genetic Algorithm (RGA). The performance of each method is analyzed in relation to several o perators types of crossover, selection and mutation, as well as in relation to the probabilities of crossover and mutation with and without dynamic ch ange of its values during the optimization process. In addition, the space reduction of the design variables and global elitism are analyzed. All GAS are effective when used with its best operations and values of parameters. For each GA, both sets of best operation types and parameters are found. Th e dynamic change of crossover and mutation probabilities, the space reducti on and the global elitism during the evolution process show that great impr ovement can be achieved for all GA types. These GAs are applied to TEAM ben chmark problem 22.