Evolving PSO algorithm design in vector fields using geometric semantic GP

Palina Bartashevich, Sanaz Mostaghim, Illya Bakurov, Leonardo Vanneschi

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

4 Citations (Scopus)
2 Downloads (Pure)

Abstract

This paper investigates the possibility of evolving new particle swarm equations representing a collective search mechanism, acting in environments with unknown external dynamics, using Geometric Semantic Genetic Programming (GSGP). The proposed method uses a novel initialization technique - the Evolutionary Demes Despeciation Algorithm (EDDA)- which allows to generate solutions of smaller size than using the traditional ramped half- and-half algorithm. We show that EDDA, using a mixture of both GP and GSGP mutation operators, allows us to evolve new search mechanisms with good generalization ability.

Original languageEnglish
Title of host publicationGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages262-263
Number of pages2
ISBN (Electronic)9781450357647
DOIs
Publication statusPublished - 6 Jul 2018
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018

Conference

Conference2018 Genetic and Evolutionary Computation Conference, GECCO 2018
CountryJapan
CityKyoto
Period15/07/1819/07/18

Keywords

  • EDDA
  • Genetic Programming
  • Geometric Semantic Mutation
  • Particle Swarm Optimization
  • Semantics
  • Vector Fields

Fingerprint Dive into the research topics of 'Evolving PSO algorithm design in vector fields using geometric semantic GP'. Together they form a unique fingerprint.

  • Cite this

    Bartashevich, P., Mostaghim, S., Bakurov, I., & Vanneschi, L. (2018). Evolving PSO algorithm design in vector fields using geometric semantic GP. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 262-263). Association for Computing Machinery, Inc. https://doi.org/10.1145/3205651.3205760