Understanding Interactions among Genetic Algorithm Parameters

Genetic algorithms GAs are multi dimensional and stochastic search methods involving complex interactions among their parameters For last two decades re searchers have been trying to understand the mechanics of GA parameter interac tions by using various techniques careful functional decomposition of parameter interactions empirical studies and Markov chain analysis Although the complex ities in these interactions are getting clearer with such analyses it still remains an open question in the mind of a new comer to the eld or to a GA practitioner as to what values of GA parameters such as population size choice of GA operators operator probabilities and others to use in an arbitrary problem In this paper we investigate the performance of simple tripartite GAs on a number of simple to complex test problems from a practical standpoint Since in a real world situation the overall time to run a GA is more or less dominated by the time consumed by objective function evaluations we compare di erent GAs for a xed number of function evaluations Based on probability calculations and simulation results it is observed that for solving simple problems unimodal or small modality problems the mutation operator plays an important role although GAs with the crossover operator alone can also solve these problems However the two operators when applied alone have two di erent working zones for the population size For com plex problems involving massive multi modality and misleadingness deception the crossover operator is the key search operator Based on these studies it is rec ommended that when in doubt the use of the crossover operator with an adequate population size is a reliable approach