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

3 Citations (Scopus)
1 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

Fingerprint

Algorithm Design
Particle swarm optimization (PSO)
Vector Field
Genetic programming
Semantics
Genetic Programming
Particle Swarm
Initialization
Mutation
Unknown
Operator

Keywords

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

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
Bartashevich, Palina ; Mostaghim, Sanaz ; Bakurov, Illya ; Vanneschi, Leonardo. / Evolving PSO algorithm design in vector fields using geometric semantic GP. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2018. pp. 262-263
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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. Association for Computing Machinery, Inc, pp. 262-263, 2018 Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, 15/07/18. https://doi.org/10.1145/3205651.3205760

Evolving PSO algorithm design in vector fields using geometric semantic GP. / Bartashevich, Palina; Mostaghim, Sanaz; Bakurov, Illya; Vanneschi, Leonardo.

GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2018. p. 262-263.

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

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Bartashevich P, Mostaghim S, Bakurov I, Vanneschi L. 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. Association for Computing Machinery, Inc. 2018. p. 262-263 https://doi.org/10.1145/3205651.3205760