Multi-Population Genetic Programming with Data Migration for Symbolic Regression

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

2 Citations (Scopus)

Abstract

In this contribution we study the effects of multi-population genetic programming for symbolic regression problems. In addition to the parallel evolution of several subpopulations according to an island model with unidirectional ring migration, the data partitions, on which the individuals are evolved, differ for every island and are adapted during algorithm execution. These modifications are intended to increase the generalization capabilities of the solutions and to maintain the genetic diversity. The effects of multiple populations as well as the used data migration strategy are compared to standard genetic programming algorithms on several symbolic regression benchmark problems.
Original languageEnglish
Title of host publicationComputational Intelligence and Efficiency in Engineering Systems
PublisherSpringer
Pages75-87
Number of pages13
Volume595
ISBN (Print)1860-949X
DOIs
Publication statusPublished - 2015

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Verlag
ISSN (Print)1860-949X

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