Abstract
In this paper, we apply a genetic algorithm to flowshop scheduling problems and examine two hybridizations of a genetic algorithm with other search algorithms. We also propose a genetic algorithm for multi-objective optimization problems. First we examine various genetic operators for the flowshop scheduling problem for minimizing the makespan. By computer simulations, we show that a two-point crossover and a shift change mutation are effective for this problem. Next we compare the genetic algorithm with other search algorithms such as local search, taboo search and simulated annealing. By computer simulations, it is shown that the genetic algorithm is a bit inferior to the others. In order to improve the performance of the genetic algorithm, we examine the hybridization of the genetic algorithm with other search algorithms. Finally, we propose a selection operator and an elitist strategy of the genetic algorithm for multi-objective problems. The high performance of our multi-objective genetic algorithm is demonstrated by computer simulations.
