An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Self-adaptation for multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Multi-Objective Evolutionary Algorithms (MOEAs) are not easy to use because they require parameter tunings to achieve good solutions and performance for an arbitrary complex problem. This paper introduces a MOEA with adaptive population size, self-adaptive crossover and self-adaptive mutation for automating the process of adjusting parameter values to make the MOEA simple to use. The new MOEA is built on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and named as Adaptable NSGA-II (ANSGA-II). Simulation results on 13 multi-objective problems demonstrate that the ANSGA-II out-performs the NSGA-II in terms of finding diverse non-dominated solutions and converging to the true Pareto-optimal front.