Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Tolerance Sensitivity and Optimality Bounds in Linear Programming
Management Science
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary multiobjective optimization using an outranking-based dominance generalization
Computers and Operations Research
Computers and Operations Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Multi-criteria optimization problems are considered where the decision maker is unable to determine the exact weights of importance of the criteria but can provide some imprecise information about these weights. Two solution concepts are studied in this framework: the optimistic min-max solution and the compromise utilitarian solution, both of which can be exactly computed for linear problems. For general problems, it is shown that these solutions can be approximated by means of a slight modification of the evolutionary algorithm NSGA-II.