Improvements in genetic algorithms

This paper presents an exhaustive study of the Simple Genetic Algorithm (SGA), Steady State Genetic Algorithm (SSGA) and Replacement Genetic Algorithm (RGA). The performance of each method is analyzed in relation to several operators types of crossover, selection and mutation, as well as in relation to the probabilities of crossover and mutation with and without dynamic change 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. The dynamic change of crossover and mutation probabilities, the space reduction and the global elitism during the evolution process show that great improvement can be achieved for all GA types. These GAs are applied to TEAM benchmark problem 22.

I. Introduction

THE USE of a genetic algorithm (GA) requires the choice of a set of genetic operations between many possibilities [1]. For example, the crossover operation with two cut points, mutation bit by bit, and selection based on the roulette well. This choice can be effective for one type of GA but worse for another. In addition, the number of generations and the population size, crossover and mutation probabilities are values that must be given to initialize the optimization process. All these parameters have great influence on the GA performance. What is the best choice is an important question. To answer this question, an exhaustive investigation is performed for the SGA, SSGA and RGA [2], [3], using three analytical test functions. Finally, the best GA's are applied to TEAM benchmark problem 22 [4]. TABLE I Genetic Operations and Others Procedures TABLE II Analytical Test Functions