Extending the Scalability of Linkage Learning Genetic Algorithms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Extending the Scalability of Linkage Learning Genetic Algorithms write by Ying-ping Chen. This book was released on 2006. Extending the Scalability of Linkage Learning Genetic Algorithms available in PDF, EPUB and Kindle. 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.
Extending the Scalability of Linkage Learning Genetic Algorithms
Extending the Scalability of Linkage Learning Genetic Algorithms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Extending the Scalability of Linkage Learning Genetic Algorithms write by Ying-ping Chen. This book was released on 2004. Extending the Scalability of Linkage Learning Genetic Algorithms available in PDF, EPUB and Kindle.
Exploitation of Linkage Learning in Evolutionary Algorithms
Exploitation of Linkage Learning in Evolutionary Algorithms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Exploitation of Linkage Learning in Evolutionary Algorithms write by Ying-ping Chen. This book was released on 2012-06-28. Exploitation of Linkage Learning in Evolutionary Algorithms available in PDF, EPUB and Kindle. One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues.
Nature-Inspired Algorithms for Optimisation
Nature-Inspired Algorithms for Optimisation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nature-Inspired Algorithms for Optimisation write by Raymond Chiong. This book was released on 2009-05-02. Nature-Inspired Algorithms for Optimisation available in PDF, EPUB and Kindle. Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume 'Nature-Inspired Algorithms for Optimisation' is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications.
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Evolutionary Computing write by A.E. Eiben. This book was released on 2015-07-01. Introduction to Evolutionary Computing available in PDF, EPUB and Kindle. The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.
