Fine Grained Population Diversity Analysis for Parallel Genetic Programming

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Abstract

In this paper we describe a formalism for estimating the structural similarity of formulas that are evolved by parallel genetic programming (GP) based identification processes. This similarity measurement can be used for measuring the genetic diversity among GP populations and, in the case of multi-population GP, the genetic diversity among sets of GP populations: The higher the average similarity among solutions becomes, the lower is the genetic diversity. Using this definition of genetic diversity for GP we test several different GP based system identification algorithms for analyzing real world measurements of a BMW Diesel engine as well as medical benchmark data taken from the UCI machine learning repository

Original languageEnglish
Title of host publicationIPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium
Number of pages8
DOIs
Publication statusPublished - 2009
EventIEEE International Parallel & Distributed Processing Symposium IPDPS 2009 - Roma, Italy
Duration: 25 May 200929 May 2009

Publication series

NameIPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium

Conference

ConferenceIEEE International Parallel & Distributed Processing Symposium IPDPS 2009
CountryItaly
CityRoma
Period25.05.200929.05.2009

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