Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
In Search of Proper Pareto-optimal Solutions Using Multi-objective Evolutionary Algorithms
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
Variable preference modeling using multi-objective evolutionary algorithms
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Preference ranking schemes in multi-objective evolutionary algorithms
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Towards a deeper understanding of trade-offs using multi-objective evolutionary algorithms
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
An evolutionary optimization approach for bulk material blending systems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Since their inception, multi-objective evolutionary algorithms have been adequately applied in finding a diverse approximation of efficient fronts of multi-objective optimization problems. In contrast, if we look at the rich history of classical multi-objective algorithms, we find that incorporation of user preferences has always been a major thrust of research. In this paper, we provide a general structure for incorporating preference information using multi-objective evolutionary algorithms. This is done in an NSGA-II scheme and by considering trade-off based preferences that come from so called proper Pareto-optimal solutions. We argue that finding proper Pareto-optimal solutions requires a set to compare with and hence, population based approaches should be a natural choice.Moreover, we suggest some practical modifications to the classical notion of proper Pareto-optimality. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of multi-objective evolutionary algorithms in finding the complete preferred region for a large class of complex problems. We also discuss a theoretical justification for our NSGA-II based framework.