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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Chen, Ying-ping (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
Edición:1st ed. 2006.
Colección:Studies in Fuzziness and Soft Computing, 190
Temas:
Artificial intelligence.
Engineering mathematics.
Engineering-Data processing.
Population genetics.
Bioinformatics.
Biotechnology.
Artificial Intelligence.
Mathematical and Computational Engineering Applications.
Population Genetics.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Introduction
  • Genetic Algorithms and Genetic Linkage
  • Genetic Linkage Learning Techniques
  • Linkage Learning Genetic Algorithm
  • Preliminaries: Assumptions and the Test Problem
  • A First Improvement: Using Promoters
  • Convergence Time for the Linkage Learning Genetic Algorithm.-Introducing Subchromosome Representations
  • Conclusions.

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