Multiple criteria decision making: the case for interdependence
Computers and Operations Research
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
On the hardness of offline multi-objective optimization
Evolutionary Computation
Objective reduction using a feature selection technique
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Real-World Applications of Multiobjective Optimization
Multiobjective Optimization
Many-Objective Optimization by Space Partitioning and Adaptive ε-Ranking on MNK-Landscapes
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Online Objective Reduction to Deal with Many-Objective Problems
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A preference-based evolutionary algorithm for multi-objective optimization
Evolutionary Computation
An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multi-level multi-objective genetic algorithm using entropy to preserve diversity
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Controlling dominance area of solutions and its impact on the performance of MOEAs
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Radar waveform optimisation as a many-objective application benchmark
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Quantifying the effects of objective space dimension in evolutionary multiobjective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A scalable multi-objective test problem toolkit
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
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Evolutionary Computation
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We present an algorithm that partitions the objective space based on an analysis of the conflict information obtained from the current Pareto front approximation. By partitioning the objectives in terms of the conflict among them, we aim to separate the multiobjective optimization into several subproblems in such a way that each of them contains the information to preserve as much as possible the structure of the original problem. We implement this framework by performing ranking and parent selection independently in each subspace. Our experimental results show that the proposed conflict-based partition strategy outperforms a similar algorithm in a test problem with independent groups of objectives. In addition, the new strategy achieves a better convergence and distribution than that produced by a strategy that creates subspaces at random. In problems in which the degree of conflict among the objectives is significantly different, the conflict-based strategy presents a better performance.