Skip to main content

The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

Albinati, Julio, Pappa, Gisele L., Otero, Fernando E.B., Oliveira, Luiz Otávio V.B. (2015) The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems. In: Genetic Programming: 18th European Conference, EuroGP 2015 Copenhagen, Denmark, April 8–10, 2015 Proceedings. Genetic Programming. Lecture Notes in Computer Science . pp. 3-15. Springer, Cham, Switzerland ISBN 978-3-319-16500-4. E-ISBN 978-3-319-16501-1. (doi:10.1007/978-3-319-16501-1_1) (KAR id:48687)


This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimize the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-3-319-16501-1_1
Uncontrolled keywords: semantic genetic programming; crossover; crossover mask optimization
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 29 May 2015 11:43 UTC
Last Modified: 09 Dec 2022 05:32 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Otero, Fernando E.B..

Creator's ORCID:
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.