Introduction to Genetic Algorithms

1. Auflage. Softcover version of original hardcover Auflage 2008
ISBN: 978-3-642-09224-4
Verlag: Springer

Buch, Englisch, 442 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 698 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Deepa, S. N.

Sivanandam, S.N.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)

Inhaltsverzeichnis

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.