Extending the Scalability of Linkage Learning Genetic Algorithms : Theory & Practice
Genetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely applied to solve problems in many disciplines. However, unable to learn linkage among genes, most GAs employed in practice nowadays suffer from the linkage problem, which refers to the need of appropr...
Guardat en:
| Autor principal: | |
|---|---|
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edició: | 1st ed. 2006. |
| Col·lecció: | Studies in Fuzziness and Soft Computing
190 |
| Accés en línia: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Extending the Scalability of Linkage Learning Genetic Algorithms, Texte imprimé, 9783540284598 • Extending the Scalability of Linkage Learning Genetic Algorithms, Texte imprimé, 9783642066719 • Extending the Scalability of Linkage Learning Genetic Algorithms, Texte imprimé, 9783540814726 • Extending the Scalability of Linkage Learning Genetic Algorithms, Texte imprimé, 9783540284598 |