Arbitrarily close alignments in the error space: A geometric semantic genetic programming approach

Ivo Gonçalves, Sara Silva, Carlos M. Fonseca, Mauro Castelli

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

8 Citations (Scopus)

Abstract

This paper shows how arbitrarily close alignments in the error space can be achieved by Genetic Programming. The consequences for the generalization ability of the resulting individuals are explored.

Original languageEnglish
Title of host publicationGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages99-100
Number of pages2
ISBN (Electronic)9781450343237
DOIs
Publication statusPublished - 20 Jul 2016
Event2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States
Duration: 20 Jul 201624 Jul 2016

Conference

Conference2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
CountryUnited States
CityDenver
Period20/07/1624/07/16

Keywords

  • Error space alignment
  • Generalization
  • Geometric semantic genetic programming
  • Overfitting

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