Foundations of Generic Optimization: Volume 1: A Combinatorial Approach to Epistasis: 20 (Mathematical Modelling: Theory and Applications, 20) - Hardcover

Iglesias, M.; Naudts, B.; Verschoren, A.; Vidal, C.

Hardcover

ISBN 10: 1402036663 ISBN 13: 9781402036668

Publisher: Springer, 2005

Synopsis

The success of a genetic algorithm when applied to an optimization problem depends upon several features present or absent in the problem to be solved, including the quality of the encoding of data, the geometric structure of the search space, deception or epistasis. This book deals essentially with the latter notion, presenting for the first time a complete state-of-the-art research on this notion, in a structured completely self-contained and methodical way.

In particular, it contains a refresher on the linear algebra used in the text as well as an elementary introductory chapter on genetic algorithms aimed at readers unacquainted with this notion.

In this way, the monograph aims to serve a broad audience consisting of graduate and advanced undergraduate students in mathematics and computer science, as well as researchers working in the domains of optimization, artificial intelligence, theoretical computer science, combinatorics and evolutionary algorithms.

About this title

Review

From the reviews:

"This book deals with combinatorial aspects of epistasis, especially normalized epistasis, a concept that exists in genetics and evolutionary algorithms. It starts with the theory of evolutionary algorithms. This illustrative introduction makes the book readable independent on other textbooks. ... The book is very well written and presents many important and useful results. ... It shows also that difficult practical problems can only be efficiently solved by a combination of Modelling, Mathematics and Computing." (Christian Posthoff, Zentralblatt MATH, Vol. 1108 (10), 2007)

Synopsis

The success of a genetic algorithm when applied to an optimization problem depends upon several features present or absent in the problem to be solved, including the quality of the encoding of data, the geometric structure of the search space, deception or epistasis. This book deals essentially with the latter notion, presenting for the first time a complete state-of-the-art research on this notion, in a structured completely self-contained and methodical way. In particular, it contains a refresher on the linear algebra used in the text as well as an elementary introductory chapter on genetic algorithms aimed at readers unacquainted with this notion. In this way, the monograph aims to serve a broad audience consisting of graduate and advanced undergraduate students in mathematics and computer science, as well as researchers working in the domains of optimization, artificial intelligence, theoretical computer science, combinatorics and evolutionary algorithms.

Edition details

• Publisher: Springer
• Publication date: 2005
• Language: English
• ISBN 10: 1402036663
• ISBN 13: 9781402036668
• Binding: Hardcover
• Number of pages: 311
• Editor: Lowen R., Verschoren A.