Fuzzy Sets and Systems - Theme: Decision and optimization
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
knowCube: a visual and interactive support for multicriteria decision making
Computers and Operations Research
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
Evolutionary Computation
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART II
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Searching for knee regions in multi-objective optimization using mobile reference points
Proceedings of the 2010 ACM Symposium on Applied Computing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An interactive territory defining evolutionary algorithm: iTDEA
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
The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making
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
IEEE Transactions on Evolutionary Computation
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A preference based interactive evolutionary algorithm for multi-objective optimization: PIE
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Interactive MOEA/D for multi-objective decision making
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Weighted preferences in evolutionary multi-objective optimization
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Comparison of design concepts in multi-criteria decision-making using level diagrams
Information Sciences: an International Journal
A preference multi-objective optimization based on adaptive rank clone and differential evolution
Natural Computing: an international journal
A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming
Applied Soft Computing
Variable and large neighborhood search to solve the multiobjective set covering problem
Journal of Heuristics
International Journal of Hybrid Intelligent Systems
Objective space partitioning using conflict information for solving many-objective problems
Information Sciences: an International Journal
Hi-index | 0.00 |
In this paper, we discuss the idea of incorporating preference information into evolutionary multi-objective optimization and propose a preference-based evolutionary approach that can be used as an integral part of an interactive algorithm. One algorithm is proposed in the paper. At each iteration, the decision maker is asked to give preference information in terms of his or her reference point consisting of desirable aspiration levels for objective functions. The information is used in an evolutionary algorithm to generate a new population by combining the fitness function and an achievement scalarizing function. In multi-objective optimization, achievement scalarizing functions are widely used to project a given reference point into the Pareto optimal set. In our approach, the next population is thus more concentrated in the area where more preferred alternatives are assumed to lie and the whole Pareto optimal set does not have to be generated with equal accuracy. The approach is demonstrated by numerical examples.