An eugenic mutations for optimum problems

Most of the genetic algorithms researchers believe that crossover is the most effective operator for most optimum problems. However, the close blood relation reproduction makes chromosomes of populations similar, then crossover between these populations becomes ineffective. The authors propose five types of mutation operators for optimum problems. Moreover, the eugenic concept is added under the assumption that the individual with higher fitness value has higher probability in breeding new better individual. They also propose a parallel processing GA and calculate the accumulate fitness value of each operator in order to select suitable operators for typical problems. The simulation results show that the a sexual reproduction with eugenic concept does search work more effective than the traditional GAs