We take into account the problem of extending the Univariate Marginal Distribution Genetic Algorithm (UMDGA) modeling and analysis to the multivariate framework. In particular, we introduce the basic general concepts and mathematical formalism to devise genetic algorithms useful to solve problems involving dependencies among genes. We state the relationships between the natural component attractors of the (numerous or infinite population) multivariate marginal distribution genetic systems and the equilibrium points of associated neural networks so rephrasing the problem of solving an evolutionary task in terms of the analysis of its properties through suitably designed neural networks.

On Multivariate Genetic Systems

CARPENTIERI, Marco
2009-01-01

Abstract

We take into account the problem of extending the Univariate Marginal Distribution Genetic Algorithm (UMDGA) modeling and analysis to the multivariate framework. In particular, we introduce the basic general concepts and mathematical formalism to devise genetic algorithms useful to solve problems involving dependencies among genes. We state the relationships between the natural component attractors of the (numerous or infinite population) multivariate marginal distribution genetic systems and the equilibrium points of associated neural networks so rephrasing the problem of solving an evolutionary task in terms of the analysis of its properties through suitably designed neural networks.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/22969
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact