抄録
In this paper, the new genetic algorithm is proposed by introducing the age structure. The genetic algorithm (GA) simulating the process of natural evolutions is an optimization method composed of genetic operators: selection, crossover and mutation. The GA has recently been demonstrated its effectiveness in the scheduling, planning and other optimization issues, but the GA has problems of premature local convergence and the bias by genetic drift, which arise from a loss of diversity in the population of the algorithm. Therefore, first, in this paper, to improve these problems of the GA we propose the genetic algorithm introducing the age structure (ASGA) which is a continuous generation model. Second, the ASGA is applied to the knapsack problem which is one of combinatorial optimization problems and compared with the simple GA (SGA). The results of numerical experiments show the effectiveness of the ASGA better than the SGA. Further, in the ASGA the relation between the lethal age and the rate of crossover is investigated through numerical experiments.
