Extending the Scalability of Linkage Learning Genetic Algorithms: Theory

Description

pic info: 2007-05-23/ExtendingGtheQScalabilityJofHLinkageGLearningRGeneticUAlgorithmsPTheoryGPracticeJStudiesQinBFuzzinessRandSSoftDComputing_orig
Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice
Springer | 3540284591 | 2005 | PDF | 1MB | RS | FF

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.

There are two primary objectives of this monograph. The first goal is to identify certain limits of genetic algorithms that use only fitness for learning genetic linkage. Both an explanatory theory and experimental results to support the theory are provided. The other goal is to propose a better designof the linkage learning genetic algorithm. After understanding the cause of the observed performance barrier, the design of the linkage learning genetic algorithm is modified accordingly to improve its performance on the problems of uniformly scaled building blocks.

This book starts with presenting the background of the linkage learning genetic algorithm. Then, it introduces the use of promoters on chromosomes to improve the performance of the linkage learning genetic algorithm on uniformly scaled problems. The convergence time model is constructed by identifying the sequential behavior, developing the tightness time model, and establishing the connection in between. The use of subchromosome representations is to avoid the limit implied by the convergence time model. The experimental results suggest that the use of subchromosome representations may be a promising way to design a better linkage learning genetic algorithm.

The study depicted in this monograph finds that using promoters on the chromosome can improve nucleation potential and promote correct buildingblock formation. It also observes that the linkage learning genetic algorithm has a consistent, sequential behavior instead of different behaviors on different problems as was previously believed. Moreover, the competition among building blocks of equal salience is the main cause of the exponential growth of convergence time. Finally, adopting subchromosome representations can reduce the competition among building blocks, and therefore, scalable genetic linkage learning for a unimetric approach is possible.

RapidShare

FileFactory

If you like it, BUY IT !!! ID117546 117546 BillAX 23 May 2007 12:25 Mirror : Download from Depositfiles.com
ID:414537 414537 Drink 23 May 2007 14:15 MIRROR : IceFile.net ID:414551 414551

Disclaimer:
Contents of this page are indexed from the Internet. All actions are under your responsability. Email us to report illegal contents or external links and we'll remove them immediately.

Please check the description for download links if any or do a search to find alternative books.

Can't Download?
Please search mirrors if you can't find download links for "Extending the Scalability of Linkage Learning Genetic Algorithms: Theory" in "Description" and someone else may update the links. Check the comments when back to find any updates.

Search Mirrors
Maybe some mirror pages will be helpful, search this book at top of this page or click here to find more info.

1. Ebooks list page : 1017
2. Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice
3. Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice
4. Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice (Studies in Fuzz
5. Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence
6. Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence
7. Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence
8. Genetic Algorithms - Principles and Perspectives: A Guide to GA Theory
9. [share_ebook] Genetic Algorithms - Principles and Perspectives: A Guide to GA Theory
10. [request_ebook] Genetic Algorithms in Search, Optimization, and Machine Learning
11. [request_ebook] THE SIMPLE GENETIC ALGORITHMS-FOUNDATION & THEORY
12. [request_ebook] Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems
13. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications (Numerical Insights)
14. Information Theory, Inference & Learning Algorithms
15. Information Theory, Inference & Learning Algorithms
16. Information Theory, Inference & Learning Algorithms