by Eisuke Kita
Publisher: InTech 2011
Number of pages: 584
Evolutionary algorithms are successively applied to wide optimization problems in the engineering, marketing, operations research, and social science, such as include scheduling, genetics, material selection, structural design and so on. Apart from mathematical optimization problems, evolutionary algorithms have also been used as an experimental framework within biological evolution and natural selection in the field of artificial life.
Home page url
Download or read it online for free here:
by L. Spector, W.B. Langdon, U. O'Reilly, P.J. Angeline - The MIT Press
Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable applications. This volume highlights the recent technical advances in this increasingly popular field.
by Adam Marczyk - The TalkOrigins Archive
Creationists argue that evolutionary processes cannot create new information, or that evolution has no practical benefits. This article disproves those claims by describing the explosive growth and widespread applications of genetic algorithms.
by Moshe Sipper - Lulu.com
Moshe Sipper and his group have produced a plethora of award-winning results, in numerous games of diverse natures, evidencing the efficiency of evolutionary algorithms in general at producing top-notch, human-competitive game strategies.
by Sebastian Ventura (ed.) - InTech
Genetic programming (GP) is a branch of Evolutionary Computing that aims the automatic discovery of programs to solve a given problem. Since its appearance, in the earliest nineties, GP has become one of the most promising paradigms ...