Skip to main content

Reducing Dimensionality to Improve Search in Semantic Genetic Programming

Oliveira, Luiz O.V.B., Miranda, Luis F., Pappa, Gisele L., Otero, Fernando E.B., Takahashi, Ricardo H.C. (2016) Reducing Dimensionality to Improve Search in Semantic Genetic Programming. In: PPSN 2016: Parallel Problem Solving from Nature – PPSN XIV. Lecture Notes in Computer Science (LNCS) . pp. 375-385. Springer ISBN 978-3-319-45822-9. E-ISBN 978-3-319-45823-6. (doi:10.1007/978-3-319-45823-6_35)

Abstract

Genetic programming approaches are moving from analysing the syntax of individual solutions to look into their semantics. One of the common definitions of the semantic space in the context of symbolic regression is a n-dimensional space, where n corresponds to the number of training examples. In problems where this number is high, the search process can became harder as the number of dimensions increase. Geometric semantic genetic programming (GSGP) explores the semantic space by performing geometric semantic operations—the fitness landscape seen by GSGP is guaranteed to be conic by construction. Intuitively, a lower number of dimensions can make search more feasible in this scenario, decreasing the chances of data overfitting and reducing the number of evaluations required to find a suitable solution. This paper proposes two approaches for dimensionality reduction in GSGP: (i) to apply current instance selection methods as a pre-process step before training points are given to GSGP; (ii) to incorporate instance selection to the evolution of GSGP. Experiments in 15 datasets show that GSGP performance is improved by using instance reduction during the evolution.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-3-319-45823-6_35
Uncontrolled keywords: Dimensionality reduction;Semantic genetic programming;Instance selection
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 04 Jul 2016 23:25 UTC
Last Modified: 01 Aug 2019 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/56211 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
  • Depositors only (login required):

Downloads

Downloads per month over past year