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
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Local dominance and controlling dominance area of solutions in multi and many objectives EAs
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A memetic model of evolutionary PSO for computational finance applications
Expert Systems with Applications: An International Journal
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
An Improved Version of Volume Dominance for Multi-Objective Optimisation
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
Many-Objective Optimization for Knapsack Problems Using Correlation-Based Weighted Sum Approach
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
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
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
A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A grid-based fitness strategy for evolutionary many-objective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Alternative fitness assignment methods for many-objective optimization problems
EA'09 Proceedings of the 9th international conference on Artificial evolution
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
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Self-controlling dominance area of solutions in evolutionary many-objective optimization
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
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
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Alleviate the hypervolume degeneration problem of NSGA-II
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Recombination of similar parents in SMS-EMOA on many-objective 0/1 knapsack problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives
Computers and Operations Research
Many-objective optimization using differential evolution with variable-wise mutation restriction
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A comparison of different algorithms for the calculation of dominated hypervolumes
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Finding a diverse set of decision variables in evolutionary many-objective optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
International Journal of Hybrid Intelligent Systems
Objective space partitioning using conflict information for solving many-objective problems
Information Sciences: an International Journal
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This work proposes a method to control the dominance area of solutions in order to induce appropriate ranking of solutions for the problem at hand, enhance selection, and improve the performance of MOEAs on combinatorial optimization problems. The proposed method can control the degree of expansion or contraction of the dominance area of solutions using a user-defined parameter S. Modifying the dominance area of solutions changes their dominance relation inducing a ranking of solutions that is different to conventional dominance. In this work we use 0/1 multiobjective knapsack problems to analyze the effects on solutions ranking caused by contracting and expanding the dominance area of solutions and its impact on the search performance of a multi-objective optimizer when the number of objectives, the size of the search space, and the complexity of the problems vary. We show that either convergence or diversity can be emphasized by contracting or expanding the dominance area. Also, we show that the optimal value of the area of dominance depends strongly on all factors analyzed here: number of objectives, size of the search space, and complexity of the problems.