Study on multi-objective genetic algorithm
Ying Gao, Lei Shi, Pingjing Yao · 2002
The multi-objective genetic algorithm (MOGA) is an effective approach in solving multi-objective optimization problems. The current multi-objective genetic algorithms are reviewed in the paper, and a new form of MOGA, steady-state non-dominated sorting genetic algorithm (SNSGA), is realized by combining the steady-state ideas in single-objective genetic algorithm (SOGA) and the fitness assignment strategy of the non-dominated sorting genetic algorithm. The fitness assignment strategy is improved and a new self-adaptive decision scheme of /spl sigma//sub share/ is proposed. This algorithm is proved to be successful with some test problems including the GA difficult problem and the GA deceptive problem.