A hierarchical approach to learning the boolean multiplexer function

by

Koza, J., R.

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Info: Foundations of Genetic Algorithms, 1991, p. 171-192
Keywords:genetic algorithms, genetic programming
Abstract:
This paper desribes the recently developed genetic programming paradigm, [GP] which genetically breeds populations of computer programs to solve problems. In genetic programming, [GP] the individuals in the population are hierarchical compositions of functions and arguments. Each of these individual computer programs is evaluated for its fitness in handling the problemenvironment. The size and shape of the computer program needed to solve the problem is not predetermined by the user, but instead emerges from the simulated evolutionary process driven by fitness. In this paper, the operation of the genetic programming paradigm [GP] is illustrated with the problem of learning the boolean 11-multiplexer function.
Notes:
FOGA-90
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BibTex:
@InCollection{Article:91:Koza:GeneticAlgoritm,
  author =       "John R. Koza",
  title =        "A hierarchical approach to learning the boolean
                 multiplexer function",
  pages =        "171--192",
  editor =       "Gregory J. E. Rawlins",
  booktitle =    "Foundations of genetic algorithms",
  publisher =    "Morgan Kaufmann",
  publisher_address = "San Mateo, California, USA",
  year =         "1991",
  address =      "Indiana University",
  month =        "15-18 " # jul # " 1990",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper desribes the recently developed genetic
                 programming paradigm, which genetically breeds
                 populations of computer programs to solve problems. In
                 genetic programming, the individuals in the population
                 are hierarchical compositions of functions and
                 arguments. Each of these individual computer programs
                 is evaluated for its fitness in handling the
                 problemenvironment. The size and shape of the computer
                 program needed to solve the problem is not
                 predetermined by the user, but instead emerges from the
                 simulated evolutionary process driven by fitness. In
                 this paper, the operation of the genetic programming
                 paradigm is illustrated with the problem of learning
                 the boolean 11-multiplexer function.",
  notes =        "FOGA-90",
}