Foundations of Generic Optimization [electronic resource] : Volume 1: A Combinatorial Approach to Epistasis /
Iglesias, M.author.
Naudts, B.author.
Verschoren, A.author.
Vidal, C.author.
Lowen, R.editor.
Verschoren, A.editor.
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 
Description based upon print version of record.
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 
Includes bibliographical references and index.
Genetic algorithms: a guide for absolute beginners -- Evolutionary algorithms and their theory -- Epistasis -- Examples -- Walsh transforms -- Multary epistasis -- Generalized Walsh transforms.
Computer science—Mathematics.
Biomathematics.
Mathematical optimization.
Combinatorics.
Mathematics of Computing.
Discrete Mathematics in Computer Science.
Genetics and Population Dynamics.
Optimization.
Combinatorics.
Mathematical Modelling: Theory and Applications