Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Timetabling the Classes of an Entire University with an Evolutionary Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Design of combinational logic circuits through an evolutionary multiobjective optimization approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A new analysis of the lebmeasure algorithm for calculating hypervolume
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
On the hardness of offline multi-objective optimization
Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Objective reduction using a feature selection technique
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Online Objective Reduction to Deal with Many-Objective Problems
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Objective reduction in evolutionary multiobjective optimization: Theory and applications
Evolutionary Computation
Study of preference relations in many-objective optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Some techniques to deal with many-objective problems
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
On the effects of adding objectives to plateau functions
IEEE Transactions on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Constrained many-objective optimization: a way forward
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
A study of multiobjective metaheuristics when solving parameter scalable problems
IEEE Transactions on Evolutionary Computation
Objective space partitioning using conflict information for many-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Adaptive objective space partitioning using conflict information for many-objective optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Advances in Engineering Software
Objective space partitioning using conflict information for solving many-objective problems
Information Sciences: an International Journal
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Most of the available multiobjective evolutionary algorithms (MOEA) for approximating the Pareto set have been designed for and tested on low dimensional problems (≤3 objectives). However, it is known that problems with a high number of objectives cause additional difficulties in terms of the quality of the Pareto set approximation and running time. Furthermore, the decision making process becomes the harder the more objectives are involved. In this context, the question arises whether all objectives are necessary to preserve the problem characteristics. One may also ask under which conditions such an objective reduction is feasible, and how a minimum set of objectives can be computed. In this paper, we propose a general mathematical framework, suited to answer these three questions, and corresponding algorithms, exact and heuristic ones. The heuristic variants are geared towards direct integration into the evolutionary search process. Moreover, extensive experiments for four well-known test problems show that substantial dimensionality reductions are possible on the basis of the proposed methodology.