Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On Using Populations of Sets in Multiobjective Optimization
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Multi-objective Supply Chain Optimization: An Industrial Case Study
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Finding exact solutions for multi-objective optimisation problems using a symbolic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
MOEA-Based approach to delayed decisions for robust conceptual design
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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This paper deals with multi-attribute classification problem based on dynamical multi-objective optimization approaches. The matching of attribute is seen as objective of the problem and user preferences are uncertain and changeable. Traditional sum weighted method and simple evolutionary algorithm are employed for experimental study over practical industry product classification problems. A integrate system framework is proposed to realize the dynamical model for multi-objective optimization. The experimental results show that classification performance system can be improved under the dynamical system framework according to user preference.