Studies on Fast Pareto Genetic Algorithm Based on Fast Fitness identification and External Population Updating Scheme

This paper investigates fast Pareto genetic algorithm based on fast fitness identification and external population updating scheme (FPGA) for searching Pareto-optimal set, which is based on a new approach of fast fitness identification algorithm for individual and a clustering on the basis of external population updating scheme to maintain population diversity and even distribution of Pareto solutions. Experiments on a set of multi-objective 0/1 knapsack optimization problems strongly shows that FPGA can obtain high-quality, well distributed non-dominated Pareto solutions with less computational efforts compared to other state-of art algorithms, and FPGA in convergence speed outperforms the representative SPEA.