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Gene Duplication to Enable Genetic Programming to Concurrently Evolve Both the Architecture and Work-Performing Steps of a Computer Program John R. Koza Stanford University Computer Science Department Stanford, Californi
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Document Date: 2006-11-17 21:18:13

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File Size: 145,89 KB

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City

Ann Arbor / San Mateo / Pollack / Cambridge / City / New York / /

Company

IBM / The MIT Press / /

Country

Netherlands / /

/

Facility

University of Michigan Press / University of Michigan / /

IndustryTerm

neural network / genetic algorithm / distributed genetic algorithm / genetic algorithms / neural networks / evolutionary algorithm / parsimonious solutions / evolutionary algorithms / messy genetic algorithm / /

OperatingSystem

Microsoft Windows / /

Organization

Stanford University Computer Science Department / University of Michigan Press / MIT / Stanford University / University of Michigan / Department of Computer and Communications Science / Department of Electrical Engineering and Computer Science / /

Person

Susumu Ohno / David Andre / Charles / John R. Koza / Walter Alden Tackett / John Holland / Arthur Samuel / J. Doyne / Kenneth E. Jr / Morgan Kaufmann / /

Position

editor / Farmer / /

ProgrammingLanguage

C / /

PublishedMedium

Complex Systems / /

Technology

Messy genetic algorithms / radiation / messy genetic algorithm / RAM / so-called distributed genetic algorithm / neural network / artificial intelligence / evolutionary algorithm / genetic algorithm / Hierarchical genetic algorithms / machine learning / Distributed Genetic Algorithm / recombination / /

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