Genetic algorithms: initialization schemes and genes extraction

Many researchers on GAs lay great emphasis on the improvements of the methods of crossover, mutation and selection. These methods include dynamic crossover and mutation rates, varying population size and varying encoding length, and so on. In this paper, however, we develop methods including the extraction operator, the uniform initialization and the unbiased initialization. The extraction operator alters the inner structure of the individual, whereas the uniform initialization and the unbiased initialization methods modify the population initialization. All of these methods are simple and can be combined with the simple genetic algorithms easily. Simulation results show that these methods exhibit an evident improvement on the performance of GAs