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EMO
2006
Springer
182views Optimization» more  EMO 2006»
13 years 9 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
13 years 11 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 6 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 3 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»
13 years 10 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