Extending the Scalability of Linkage Learning Genetic Algorithms Theory & Practice /

• Main Author: Chen, Ying-ping (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
• Series: Studies in Fuzziness and Soft Computing, 190
• Subjects: Computer science.; Biotechnology.; Artificial intelligence.; Bioinformatics.; Biomathematics.; Applied mathematics.; Engineering mathematics.; Computer Science.; Artificial Intelligence (incl. Robotics).; Appl.Mathematics/Computational Methods of Engineering.; Genetics and Population Dynamics.

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...

Table of Contents:

• 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.