Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Robust multi-objective optimization in high dimensional spaces
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
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Online Objective Reduction to Deal with Many-Objective Problems
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
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
IEEE Transactions on Evolutionary Computation
Multi-objective control systems design with criteria reduction
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
Multi-objective optimization with joint probabilistic modeling of objectives and variables
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems
Annals of Mathematics and Artificial Intelligence
Objective space partitioning using conflict information for solving many-objective problems
Information Sciences: an International Journal
Hi-index | 0.00 |
This paper introduces two new algorithms to reduce the number of objectives in a multiobjective problem by identifying the most conflicting objectives. The proposed algorithms are based on a feature selection technique proposed by Mitra et. al. [11]. One algorithm is intended to determine the minimum subset of objectives that yields the minimum error possible, while the other finds a subset of objectives of a given size that yields the minimum error. To validate these algorithms we compare their results against those obtained by two similar algorithms recently proposed. The comparative study shows that our algorithms are very competitive with respect to the reference algorithms. Additionally, our approaches require a lower computational time. Also, in this study we propose to use the inverted generational distance to evaluate the quality of a subset of objectives.