Extending the Scalability of Linkage Learning Genetic Algorithms di Ying-Ping Chen edito da Springer Berlin Heidelberg
Alta reperibilità

Extending the Scalability of Linkage Learning Genetic Algorithms

Theory & Practice

EAN:

9783642066719

ISBN:

3642066712

Pagine:
140
Formato:
Paperback
Lingua:
Tedesco
Acquistabile con o la

Descrizione Extending the Scalability of Linkage Learning Genetic Algorithms

Genetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely applied to solve problems in many disciplines. However, most GAs employed in practice nowadays are unable to learn genetic linkage and suffer from the linkage problem. The linkage learning genetic algorithm (LLGA) was proposed to tackle the linkage problem with several specially designed mechanisms. While the LLGA performs much better on badly scaled problems than simple GAs, it does not work well on uniformly scaled problems as other competent GAs. Therefore, we need to understand why it is so and need to know how to design a better LLGA or whether there are certain limits of such a linkage learning process. This book aims to gain better understanding of the LLGA in theory and to improve the LLGA's performance in practice. It starts with a survey of the existing genetic linkage learning techniques and describes the steps and approaches taken to tackle the research topics, including using promoters, developing the convergence time model, and adopting subchromosomes.

Spedizione gratuita
€ 142.30€ 149.79
Risparmi:€ 7.49(5%)
o 3 rate da € 47.43 senza interessi con
Disponibile in 10-12 giorni
servizio Prenota Ritiri su libro Extending the Scalability of Linkage Learning Genetic Algorithms
Prenota e ritira
Scegli il punto di consegna e ritira quando vuoi

Recensioni degli utenti

e condividi la tua opinione con gli altri utenti