Genetic Algorithms as Global Random Search Methods

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is ar_md that tim similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions aml then exploiting their similarities. Proportional selection is characterized a.s a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search bre,_lth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.