Extending the Scalability of Linkage Learning Genetic Algorithms

Extending the Scalability of Linkage Learning Genetic Algorithms
Title Extending the Scalability of Linkage Learning Genetic Algorithms PDF eBook
Author Ying-ping Chen
Publisher Springer Science & Business Media
Pages 152
Release 2006
Genre Computers
ISBN 9783540284598

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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
Title Extending the Scalability of Linkage Learning Genetic Algorithms PDF eBook
Author Ying-ping Chen
Publisher
Pages 120
Release 2006
Genre Genetic algorithms
ISBN 9780978354022

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Nature-Inspired Algorithms for Optimisation

Nature-Inspired Algorithms for Optimisation
Title Nature-Inspired Algorithms for Optimisation PDF eBook
Author Raymond Chiong
Publisher Springer
Pages 524
Release 2009-05-02
Genre Technology & Engineering
ISBN 3642002676

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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.

Advances in Evolutionary Algorithms

Advances in Evolutionary Algorithms
Title Advances in Evolutionary Algorithms PDF eBook
Author Chang Wook Ahn
Publisher Springer
Pages 180
Release 2007-05-22
Genre Technology & Engineering
ISBN 3540317597

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Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. This book provides effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.

Introduction to Evolutionary Computing

Introduction to Evolutionary Computing
Title Introduction to Evolutionary Computing PDF eBook
Author A.E. Eiben
Publisher Springer
Pages 294
Release 2015-07-01
Genre Computers
ISBN 3662448742

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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.

Rule-Based Evolutionary Online Learning Systems

Rule-Based Evolutionary Online Learning Systems
Title Rule-Based Evolutionary Online Learning Systems PDF eBook
Author Martin V. Butz
Publisher Springer
Pages 279
Release 2006-01-04
Genre Computers
ISBN 3540312315

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Rule-basedevolutionaryonlinelearningsystems,oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces,andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis,understanding,anddesign;(2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland’s originally envisioned cognitive systems. Martin V.

Innovations in Machine Learning

Innovations in Machine Learning
Title Innovations in Machine Learning PDF eBook
Author Dawn E. Holmes
Publisher Springer Science & Business Media
Pages 285
Release 2006-03-09
Genre Computers
ISBN 3540306099

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Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.