Axiomatic foundation of the analytic hierarchy process
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Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Journal of Complexity
Machine Learning
Machine Learning - Special issue on inductive transfer
Interactive multiple objective programming using Tchebycheff programs and artificial neural networks
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Machine Learning
Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The Quantitative Importance of Criteria with Discrete First-Order Metric Scale
Automation and Remote Control
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Introduction to Multiobjective Optimization: Interactive Approaches
Multiobjective Optimization
Interactive Multiobjective Optimization Using a Set of Additive Value Functions
Multiobjective Optimization
Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization
Multiobjective Optimization
Interactive Multiobjective Evolutionary Algorithms
Multiobjective Optimization
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Active learning for directed exploration of complex systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Journal of Artificial Intelligence Research
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Adapting to a realistic decision maker: experiments towards a reactive multi-objective optimizer
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
Interactive MOEA/D for multi-objective decision making
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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The centrality of the decision maker (DM) is widely recognized in the multiple criteria decision-making community. This translates into emphasis on seamless human-computer interaction, and adaptation of the solution technique to the knowledge which is progressively acquired from the DM. This paper adopts the methodology of reactive search optimization (RSO) for evolutionary interactive multiobjective optimization. RSO follows to the paradigm of "learning while optimizing," through the use of online machine learning techniques as an integral part of a self-tuning optimization scheme. User judgments of couples of solutions are used to build robust incremental models of the user utility function, with the objective to reduce the cognitive burden required from the DM to identify a satisficing solution. The technique of support vector ranking is used together with a k-fold cross-validation procedure to select the best kernel for the problem at hand, during the utility function training procedure. Experimental results are presented for a series of benchmark problems.