Advances in genetic programming. [Vol. 1] / edited by Kenneth E. Kinnear, Jr.
1994
QA76.7
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Details
Title
Advances in genetic programming. [Vol. 1] / edited by Kenneth E. Kinnear, Jr.
ISBN
9780262277181
0262277182
9780262111881 (hardcover)
0262111888 (hardcover)
9780262515535 (paperback)
0262515539 (paperback)
0262277182
9780262111881 (hardcover)
0262111888 (hardcover)
9780262515535 (paperback)
0262515539 (paperback)
Published
Cambridge, Massachusetts : The MIT Press, [1994]
Copyright
©1994
Language
English
Description
1 online resource (ix, 476 pages) : illustrations.
Call Number
QA76.7
Dewey Decimal Classification
006.3
Summary
There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm.There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm.Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail.A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and "demes" to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality.Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed.
Note
"A Bradford book."
Available through MITCogNet.
Available through MITCogNet.
Access Note
Access limited to authorized users.
Source of Description
OCLC-licensed vendor bibliographic record.
Added Author
Kinnear, Kenneth E., editor.
Added Corporate Author
MITCogNet.
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