Proceedings ArticleDOI
Handling preferences in evolutionary multiobjective optimization: a survey
Carlos A. Coello Coello
- Vol. 1, pp 30-37
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The most important preference handling approaches used with evolutionary algorithms, analyzing their advantages and disadvantages are reviewed, and then, some of the potential areas of future research in this discipline are proposed.Abstract:
Despite the relatively high volume of research conducted on evolutionary multiobjective optimization in the last few years. Little attention has been paid to the decision making process that is required to select a final solution to the multiobjective optimization problem at hand. This paper reviews the most important preference handling approaches used with evolutionary algorithms, analyzing their advantages and disadvantages, and then, it proposes some of the potential areas of future research in this discipline.read more
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References
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Muiltiobjective optimization using nondominated sorting in genetic algorithms
N. Srinivas,Kalyanmoy Deb +1 more
TL;DR: Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
Proceedings Article
Genetic algorithms for multi-objective optimization: formulation, discussion, and generalization
C M Fonseca,P J Fleming +1 more
Proceedings Article
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
TL;DR: A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.
Posted Content
Decisions with Multiple Objectives
Ralph L. Keeney,Howard Raiffa +1 more
TL;DR: In this article, a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives.
Journal ArticleDOI
An overview of evolutionary algorithms in multiobjective optimization
TL;DR: Current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality.