A novel distributed genetic algorithm implementation with variable number of islands
Takuma Jumonji, Goutam Chakraborty, Hiroshi Mabuchi, Masafumi Matsuhara · 2007
Genetic algorithm (GA) has some inherent drawbacks which become apparent while trying to solve complex multimodal problems. They are slow and the efficiency depends on parameter values. Some methods were proposed for alleviating these problems. But they did not address all the drawbacks. In this work, we propose a new distributed implementation strategy named variable island GA (VIGA), where the number of islands vary. In VIGA, where the number of individuals in every island is 2, the parameter population size in an island is fixed. Other parameters like number of islands, crossover/mutation probabilities, also need not be set. As the generation progresses, islands are created or erased based on the convergence status of searching in each island. Experiments were done with different function optimization problems. For all experiments VIGA delivered better or at least as good results as obtained by other competitive algorithms, at the expense of less computation and communication costs.