A Study of Dynamic Populations in Geometric Semantic Genetic Programming

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
13 Downloads (Pure)

Abstract

Allowing the population size to variate during the evolution can bring advantages to evolutionary algorithms (EAs), retaining computational effort during the evolution process. Dynamic populations use computational resources wisely in several types of EAs, including genetic programming. However, so far, a thorough study on the use of dynamic populations in Geometric Semantic Genetic Programming (GSGP) is missing. Still, GSGP is a resource-greedy algorithm, and the use of dynamic populations seems appropriate. This paper adapts algorithms to GSGP to manage dynamic populations that were successful for other types of EAs and introduces two novel algorithms. The novel algorithms exploit the concept of semantic neighbourhood. These methods are assessed and compared through a set of eight regression problems. The results indicate that the algorithms outperform standard GSGP, confirming the suitability of dynamic populations for GSGP. Interestingly, the novel algorithms that use semantic neighbourhood to manage variation in population size are particularly effective in generating robust models even for the most difficult of the studied test problems.
Original languageEnglish
Article number119513
Pages (from-to)1-21
Number of pages21
JournalInformation Sciences
Volume648
Issue numberNovember
DOIs
Publication statusPublished - 1 Nov 2023

Keywords

  • Dynamic Populations
  • Genetic Programming
  • Geometric semantic genetic programming
  • Semantic neighbourhood

Fingerprint

Dive into the research topics of 'A Study of Dynamic Populations in Geometric Semantic Genetic Programming'. Together they form a unique fingerprint.

Cite this