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EMO
2006
Springer
182views Optimization» more  EMO 2006»
14 years 1 months ago
Multi-objective Pole Placement with Evolutionary Algorithms
Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new...
Gustavo Sánchez, Minaya Villasana, Miguel S...
IJCNN
2006
IEEE
14 years 3 months ago
An Evaluation of Over-Fit Control Strategies for Multi-Objective Evolutionary Optimization
— The optimization of classification systems is often confronted by the solution over-fit problem. Solution over-fit occurs when the optimized classifier memorizes the traini...
Paulo Vinicius Wolski Radtke, Tony Wong, Robert Sa...
ICAI
2003
13 years 11 months ago
Exploiting the Marginal Profits of Constraints with Evolutionary Multi-Objective Optimization Techniques
Many real-world search and optimization problems naturally involve constraint handling. Recently, quite a few heuristic methods were proposed to solve the nonlinear constrained op...
Zhenyu Yan, Wei Zhi, Lishan Kang
PPSN
2010
Springer
13 years 8 months ago
Optimizing Delivery Time in Multi-Objective Vehicle Routing Problems with Time Windows
The Vehicle Routing Problem with Time Windows involves finding the lowest-cost set of routes to deliver goods to customers, which have service time windows, using a homogeneous ...
Abel Garcia-Najera, John A. Bullinaria
EMO
2005
Springer
110views Optimization» more  EMO 2005»
14 years 3 months ago
Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms
Abstract. While Single-Objective Evolutionary Algorithms (EAs) parallelization schemes are both well established and easy to implement, this is not the case for Multi-Objective Evo...
Felix Streichert, Holger Ulmer, Andreas Zell