Decision theory: an introduction to the mathematics of rationality
Decision theory: an introduction to the mathematics of rationality
The use of fuzzy outranking relations in preference modelling
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
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
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
Evolutionary multiobjective optimization using an outranking-based dominance generalization
Computers and Operations Research
| Hi-index | 0.98 |
Up to now, classical decision models and fuzzy approaches for decision-making have been seen as mutually inconsistent proposals. Based on bivalent 0-1 logic, classical decision-making approaches should be particular cases of decision models which consider fuzzy preferences. Here, a new method for deriving a final prescription from a fuzzy preference relation is proposed, which satisfies the so-called Principle of Correspondence. This proposal is more robust than others in relation to irrelevant alternatives, minimizing contradictions between the final prescription and the global model of a decision-maker's preferences previously captured in a fuzzy outranking relation. The resulting complex multiobjective optimization problem is solved by using an operational evolutionary algorithm.