Evolving Compact Solutions in Genetic Programming: A Case Study   [GP]

by

Blickle, T.

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Info: 1996
Keywords:genetic algorithms, genetic programming
Abstract:
Genetic programming (GP) [GP] is a variant of genetic algorithms [GA] where the data structures [DS] handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression [SR] is the determination of a function dependence $y=g(\bf x)$ that approximates a set of data points ($\bf x_i,y_i$). In this paper the feasibility of symbolic regression [SR] with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data.
Notes:
Presented at PPSN 4
URL(s):(G)zipped postscript

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BibTex:
@TechReport{blickle:1996:ecs,
  author =       "Tobias Blickle",
  title =        "Evolving Compact Solutions in Genetic Programming: {A}
                 Case Study",
  institution =  "TIK Institut fur Technische Informatik und
                 Kommunikationsnetze, Computer Engineering and Networks
                 Laboratory, ETH, Swiss Federal Institute of
                 Technology",
  year =         "1996",
  type =         "TIK-Report",
  address =      "Gloriastrasse 35, 8092 Zurich, Switzerland",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.tik.ee.ethz.ch/~blickle/ppsn1.ps.gz",
  abstract =     "Genetic programming (GP) is a variant of genetic
                 algorithms where the data structures handled are trees.
                 This makes GP especially useful for evolving functional
                 relationships or computer programs, as both can be
                 represented as trees. Symbolic regression is the
                 determination of a function dependence $y=g({\bf x})$
                 that approximates a set of data points (${\bf
                 x_i},y_i$). In this paper the feasibility of symbolic
                 regression with GP is demonstrated on two examples
                 taken from different domains. Furthermore several
                 suggested methods from literature are compared that are
                 intended to improve GP performance and the readability
                 of solutions by taking into account introns or
                 redundancy that occurs in the trees and keeping the
                 size of the trees small. The experiments show that GP
                 is an elegant and useful tool to derive complex
                 functional dependencies on numerical data.",
  notes =        "Presented at PPSN 4

                 ",
  size =         "10 pages",
}