# Evolving Compact Solutions in Genetic Programming: A Case Study

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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.handshake.de/user/blickle/publications/ppsn1.ps",
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",
}



Genetic Programming entries for Tobias Blickle

Citations