Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
New ideas in applying scatter search to multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
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.