TACKLING REAL-CODED GENETIC ALGORITHMS - OPERATORS AND TOOLS FOR BEHAVIORAL-ANALYSIS

Genetic algorithms play a significant role, as search techniques for h andling complex spaces, in many fields such as artificial intelligence , engineering, robotic, etc. Genetic algorithms are based on the under lying genetic process in biological organisms and on the natural evolu tion principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three ope rations: selection, crossover and mutation. Under its initial formulat ion, the search space solutions are coded using the binary alphabet. H owever, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem p articularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the feat ures of real-coded genetic algorithms. Different models of genetic ope rators and some mechanisms available for studying the behaviour of thi s type of genetic algorithms are revised and compared.