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

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## 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 Review item: Mark as doublet (will be reviewed)

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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", }