Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Constrained Test Problems for Multi-objective Evolutionary Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A framework for incorporating trade-off information using multi-objective evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
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
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There are multiple solution concepts in multi-objective optimization among which a decision maker has to select some good solutions usually which satisfy some trade-off criteria's. The need for potentially good solutions has always been one of the primary aims in multi-objective optimization. A complete representation of all these solutions is only possible with population based approaches like multi-objective evolutionary algorithms since then trade-off's can be calculated at each generation from the population members. Thus this paper proposes the use of multi-objective evolutionary algorithms for obtaining a complete representation of these good solutions. Theoretical results show how one can integrate search procedure for obtaining these solutions in population based evolutionary algorithms and some convergence results. Finally simulation results are presented on a number of test problems.