- Full Description
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.
- Table of Contents
Table of Contents
- 0: Genetic agorithms: a guide for absolute beginners.
- I: Evolutionary algorithms and their theory.
- II: Epistasis.
- III: Examples.
- IV: Walsh transforms.
- V: Multary epistasis.
- VI: Generalized Walsh transforms.
- A: The schema theorem (variations on a theme).
- B: Algebraic background.
If you think that you've found an error in this book, please let us know by emailing to firstname.lastname@example.org . You will find any confirmed erratum below, so you can check if your concern has already been addressed. No errata are currently published