Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
Evolutionary Computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
An Evolutionary Metaheuristic for Approximating Preference-Nondominated Solutions
INFORMS Journal on Computing
Journal of Artificial Intelligence Research
A preference-based evolutionary algorithm for multi-objective optimization
Evolutionary Computation
A favorable weight-based evolutionary algorithm for multiple criteria problems
IEEE Transactions on Evolutionary Computation
A territory defining multiobjective evolutionary algorithms and preference incorporation
IEEE Transactions on Evolutionary Computation
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Interactive MOEA/D for multi-objective decision making
Proceedings of the 13th annual conference on Genetic and evolutionary computation
INSPM: An interactive evolutionary multi-objective algorithm with preference model
Information Sciences: an International Journal
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We develop a preference-based multiobjective evolutionary algorithm that interacts with the decision maker (DM) during the course of optimization. We create a territory around each solution where no other solutions are allowed. We define smaller territories around the preferred solutions in order to obtain denser coverage of these regions. At each interaction, the algorithm asks the DM to choose his/her best solution among a set of representative solutions to guide the search toward the neighborhood of the selected solution. The algorithm aims to converge to a final preferred region of the DM. We test the algorithm on three problems using three different utility function types to simulate the DM's responses. The results show that the algorithm converges the DM's simulated preferred regions well.