
Introduction to Genetic Algorithms
Springer (Publisher)
Published on 15. October 2010
Book
Paperback/Softback
XIX, 442 pages
978-3-642-09224-4 (ISBN)
Description
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.
More details
Edition
Softcover reprint of hardcover 1st ed. 2008
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XIX, 442 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 25 mm
Weight
698 gr
ISBN-13
978-3-642-09224-4 (9783642092244)
DOI
10.1007/978-3-540-73190-0
Schweitzer Classification
Other editions
Additional editions

S.N. Sivanandam | S. N. Deepa
Introduction to Genetic Algorithms
Book
10/2007
1st Edition
Springer
€171.19
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Content
Evolutionary Computation.- Genetic Algorithms.- Terminologies and Operators of GA.- Advanced Operators and Techniques in Genetic Algorithm.- Classification of Genetic Algorithm.- Genetic Programming.- Genetic Algorithm Optimization Problems.- Genetic Algorithm Implementation Using Matlab.- Genetic Algorithm Optimization in C/C++.- Applications of Genetic Algorithms.- to Particle Swarm Optimization and Ant Colony Optimization.