A schema theory analysis of the evolution of size in genetic programming with linear representations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

In this paper we use the schema theory presented in [20] to better understand the changes in size distribution when using GP with standard crossover and linear structures. Applications of the theory to problems both with and without fitness suggest that standard crossover induces specific biases in the distributions of sizes, with a strong tendency to over sample small structures, and indicate the existence of strong redistribution effects that may be a major force in the early stages of a GP run. We also present two important theoretical results: An exact theory of bloat, and a general theory of how average size changes on flat landscapeswith glitches. The latter implies the surprising result that a single program glitch in an otherwise flat fitness landscape is sufficient to drive the average program size of an infinite population, which may have important implications for the control of code growth.

Original languageEnglish (US)
Title of host publicationGenetic Programming - 4th European Conference, EuroGP 2001, Proceedings
EditorsMarco Tomassini, Conor Ryan, William B. Langdon, Pier Luca Lanzi, Andrea G.B. Tettamanzi, Julian Miller
PublisherSpringer- Verlag
Pages108-125
Number of pages18
ISBN (Electronic)3540418997, 9783540418993
StatePublished - Jan 1 2001
Event4th European Conference on Genetic Programming, EuroGP 2001 - Lake Como, Italy
Duration: Apr 18 2001Apr 20 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2038
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th European Conference on Genetic Programming, EuroGP 2001
Country/TerritoryItaly
CityLake Como
Period4/18/014/20/01

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