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Genetic programming 1996 : Proceedings of the First Annual Conference, July 28-31, 1996, Stanford University

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Genetic programming is a domain-independent method for automatic programming that evolves computer programs that solve, or approximately solve, problems. Starting with a primordial ooze of thousands of randomly created computer programs composed of functions and terminals appropriate to a problem, a population of programs is progressively evolved over many generations using the Darwinian principle of survival of the fittest, a sexual recombination operation, and occasional mutation.

These proceedings of the first Genetic Programming Conference present the most recent research in the field of genetic programming as well as recent research results in the fields of genetic algorithms, evolutionary programming, and learning classifier systems.

Topics include: Applications of genetic programming. Theoretical foundations of genetic programming. Implementation issues. Technique extensions. Automated synthesis of analog electrical circuits. Automatic programming of cellular automata. Induction. System identification. Control. Evolution of machine language programs. Automatic programming of multi-agent strategies. Automated evolution of program architecture. Evolution of mental models. Implementations of memory and state. Cellular encoding. Evolvable hardware. Parallelization techniques. Relations to biology and cognitive systems. Genetic algorithms. Evolutionary programming. Evolution strategies. Learning classifier systems.

Bradford Books imprint

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Detail Information

Series Title

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Call Number

005 GEN

Publisher

Cambridge, Mass. : : MIT Press,., 1996.

Collation

1 online resource (xviii, 568 pages) : illustrations.

Language

English

ISBN/ISSN

9780262315876

Classification

005

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

computer programming
Genetic algorithms
Evolutionary programming (Computer science)

Specific Detail Info

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Statement of Responsibility

edited by John R. Koza [and others].

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Source

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Validator

maya

Digital Object Identifier (DOI)

https://doi.org/10.7551/mitpress/3242.001.0001

Journal Volume

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Journal Issue

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