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IJCNN
2006
IEEE
14 years 2 months ago
Generalization Improvement in Multi-Objective Learning
— Several heuristic methods have been suggested for improving the generalization capability in neural network learning, most of which are concerned with a single-objective (SO) l...
Lars Gräning, Yaochu Jin, Bernhard Sendhoff
HAIS
2009
Springer
14 years 18 days ago
Hybrid Evolutionary Algorithm for Solving Global Optimization Problems
Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked...
Radha Thangaraj, Millie Pant, Ajith Abraham, Youak...
CORR
2008
Springer
91views Education» more  CORR 2008»
13 years 8 months ago
Randomised Variable Neighbourhood Search for Multi Objective Optimisation
Various local search approaches have recently been applied to machine scheduling problems under multiple objectives. Their foremost consideration is the identification of the set ...
Martin Josef Geiger
EC
2012
289views ECommerce» more  EC 2012»
12 years 3 months ago
Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm
In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have a better knowledge about different optima...
Kalyanmoy Deb, Amit Saha
GECCO
2007
Springer
532views Optimization» more  GECCO 2007»
14 years 2 months ago
Evolving evolutionary algorithms using evolutionary algorithms
A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every...
Laura Diosan, Mihai Oltean