C.-H. Lin and P.-l. Lin (Taiwan)
Gene evaluation, NSGA-II, Multi-objective optimization, Pareto-optimal solutions
This study proposes a new evaluation method to improve the non-dominate sorting genetic algorithm-II (NSGA-II), which is a well-known algorithm for finding the Pareto optimal set of multi-objective optimization problems. To further enhance the advantages of fast non-dominate sorting and diversity preservation in the existing NSGA-II, an evaluative crossover is introduced in this paper to incorporate with NSGA-II to retain superior schema patterns in each chromosome for solving multi-objective problems. Each crossover gene is mutually exchanged and evaluated by its contribution in the mutual-evaluation method. Experiments on five well-known benchmark problems of diverse complexities show that the proposed algorithm can find Pareto-optimal solutions in all test cases. Compared with four existing algorithms, the proposed algorithm can achieve better convergence and diversity qualities with a considerable effort reduction in explicit function analyses.
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