A new parallel distributed genetic algorithm applied to traveling salesman problems
Mitsunori Miki, Tomoyuki Hiroyasu, Takanori Mizuta · 2001
This paper proposes a new method of genetic algorithms (GAs) for dicrete optimization problems. For continuous problems, it has been reported that parallel distributed genetic algorithms (PDGAs) show higher performance than conventional GAs. But, for discrete optimization problems, the performance of PDGAs has not been clearly shown. We examine the performance of conventional GAs, distributed GAs, and the proposed method for a typical optimization problem, the Traveling Salesman Problem(TSP). The features of the proposed method are based on multiple crossover operations applied to the entire population (Centralized Multiple Crossover: CMX) and the isolated DGA.