Introduction to Genetic Algorithms

S.N. Sivanandam

ISBN 10: 354073189X / ISBN 13: 9783540731894

Published by Springer, Berlin, Springer Berlin Heidelberg, Springer, 2007

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Theoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are not completely understood (even nowadays), there exist some points supported by strong experimental evidence: - Evolution is a process operating over chromosomes rather than over organisms. The former are organic tools encoding the structure of a living being, i.e., a cr- ture is 'built' decoding a set of chromosomes. - Natural selection is the mechanism that relates chromosomes with the ef ciency of the entity they represent, thus allowing that ef cient organism which is we- adapted to the environment to reproduce more often than those which are not. - The evolutionary process takes place during the reproduction stage. There exists a large number of reproductive mechanisms in Nature. Most common ones are mutation (that causes the chromosomes of offspring to be different to those of the parents) and recombination (that combines the chromosomes of the parents to produce the offspring). Based upon the features above, the three mentioned models of evolutionary c- puting were independently (and almost simultaneously) developed. 442 pp. Englisch.

Seller Inventory # 9783540731894

Bibliographic Details

• Title: Introduction to Genetic Algorithms
• Publisher: Springer, Berlin, Springer Berlin Heidelberg, Springer
• Publication Date: 2007
• Language: English
• ISBN 10: 354073189X
• ISBN 13: 9783540731894
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 158H x 38D millimeters 241 millimeters width by 158 millimeters height by 38 millimeters depth
• Weight: 826 grams

About this title

Synopsis

Theoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are not completely understood (even nowadays), there exist some points supported by strong experimental evidence: • Evolution is a process operating over chromosomes rather than over organisms. The former are organic tools encoding the structure of a living being, i.e., a cr- ture is “built” decoding a set of chromosomes. • Natural selection is the mechanism that relates chromosomes with the ef ciency of the entity they represent, thus allowing that ef cient organism which is we- adapted to the environment to reproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage. There exists a large number of reproductive mechanisms in Nature. Most common ones are mutation (that causes the chromosomes of offspring to be different to those of the parents) and recombination (that combines the chromosomes of the parents to produce the offspring). Based upon the features above, the three mentioned models of evolutionary c- puting were independently (and almost simultaneously) developed.

From the Back Cover

Genetic Algorithms are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of Genetic Algorithms is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. This book is designed to provide an in-depth knowledge on the basic operational features and characteristics of Genetic Algorithms. The various operators and techniques given in the book are pertinent to carry out Genetic Algorithm Research Projects. The book also explores the different types are Genetic Algorithms available with their importance. Implementation of Genetic Algorithm concept has been performed using the universal language C/C++ and the discussion also extends to Genetic Algorithm MATLAB Toolbox. Few Genetic Algorithm problems are programmed using MATLAB and the simulated results are given for the ready reference of the reader. The applications of Genetic Algorithms in Machine learning, Mechanical Engineering, Electrical Engineering, Civil Engineering, Data Mining, Image Processing, and VLSI are dealt to make the readers understand where the concept can be applied.