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JIFS
2002
107views more  JIFS 2002»
14 years 9 months ago
Model selection via Genetic Algorithms for RBF networks
This work addresses the problem of finding the adjustable parameters of a learning algorithm using Genetic Algorithms. This problem is also known as the model selection problem. In...
Estefane G. M. de Lacerda, André Carlos Pon...
ITICSE
1997
ACM
15 years 1 months ago
A genetic algorithms tutorial tool for numerical function optimisation
The field of Genetic Algorithms has grown into a huge area over the last few years. Genetic Algorithms are adaptive methods, which can be used to solve search and optimisation pro...
Edmund K. Burke, D. B. Varley
GECCO
2006
Springer
188views Optimization» more  GECCO 2006»
15 years 1 months ago
Dominance learning in diploid genetic algorithms for dynamic optimization problems
This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus i...
Shengxiang Yang
GECCO
2008
Springer
129views Optimization» more  GECCO 2008»
14 years 10 months ago
Estimation of pump-curves using genetic algorithms
This paper presents a variety of different ways of estimating the general parameters for pump-curves. First a formulation is made that converts the problem into estimating four p...
Gerulf K. M. Pedersen, Zhenyu Yang
GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
15 years 2 months ago
Evolution, development and learning using self-modifying cartesian genetic programming
Self-Modifying Cartesian Genetic Programming (SMCGP) is a form of genetic programming that integrates developmental (self-modifying) features as a genotype-phenotype mapping. This...
Simon Harding, Julian Francis Miller, Wolfgang Ban...