Advances in applying genetic programming to machine learning, focussing on classification problems

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6 Citations (Scopus)

Abstract

A Genetic Programming based approach for solving classification problems is presented in this paper. Classification is understood as the act of placing an object into a set of categories, based on the object's properties; classification algorithms are designed to learn a function which maps a vector of object features into one of several classes. This is done by analyzing a set of input-output examples ("training samples") of the function. Here we present a method based on the theory of Genetic Algorithms and Genetic Programming that interprets classification problems as optimization problems: Each presented instance of the classification problem is interpreted as an instance of an optimization problem, and a solution is found by a heuristic optimization algorithm. The major new aspects presented in this paper are suitable genetic operators for this problem class (mainly the creation of new hypotheses by merging already existing ones and their detailed evaluation) we have designed and implemented. The experimental part of the paper documents the results produced using new hybrid variants of genetic algorithms as well as investigated parameter settings.

Original languageEnglish
Title of host publication20th International Parallel and Distributed Processing Symposium, IPDPS 2006
PublisherIEEE Computer Society
Pages1-10
ISBN (Print)1424400546, 9781424400546
DOIs
Publication statusPublished - 2006
Event20th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2006 - Rhodes Island, Greece
Duration: 25 Apr 200629 Apr 2006

Publication series

Name20th International Parallel and Distributed Processing Symposium, IPDPS 2006
Volume2006

Conference

Conference20th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2006
CountryGreece
CityRhodes Island
Period25.04.200629.04.2006

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