An aspiration-level interactive model for multiple criteria decision making
Computers and Operations Research - Special issue: implementing multiobjective optimization methods: behavioral and computational issues
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
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
A new scheme for interactive multi-criteria decision making
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Interactive Evolutionary Multiobjective Optimization Using Robust Ordinal Regression
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Preference-based multi-objective particle swarm optimization using desirabilities
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
An interactive evolutionary multi-objective optimization method based on polyhedral cones
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Computers and Operations Research
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This chapter describes various approaches to the use of evolutionary algorithms and other metaheuristics in interactive multiobjective optimization. We distinguish the traditional approach to interactive analysis with the use of single objective metaheuristics, the semi-a posteriori approach with interactive selection from a set of solutions generated by a multiobjective metaheuristic, and specialized interactive multiobjective metaheuristics in which the DM's preferences are interactively expressed during the run of the method. We analyze properties of each of the approaches and give examples from the literature.