Evolving PSO algorithm design in vector fields using geometric semantic GP

Palina Bartashevich; Sanaz Mostaghim; Illya Bakurov; Leonardo Vanneschi

doi:10.1145/3205651.3205760

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 proceeding › Conference contribution › peer-review

6 Citations (Scopus)

7 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 language	English
Title of host publication	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
Publisher	Association for Computing Machinery, Inc
Pages	262-263
Number of pages	2
ISBN (Electronic)	9781450357647
DOIs	https://doi.org/10.1145/3205651.3205760
Publication status	Published - 6 Jul 2018
Event	2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan Duration: 15 Jul 2018 → 19 Jul 2018

Conference

Conference	2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Country/Territory	Japan
City	Kyoto
Period	15/07/18 → 19/07/18

Keywords

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

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10.1145/3205651.3205760Licence: Other

Bartashevich_Mostaghim_Bakurov_Vanneschi_Evolving_2018Final published version, 650 KBLicence: Other

Cite this

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title = "Evolving PSO algorithm design in vector fields using geometric semantic GP",

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.",

keywords = "EDDA, Genetic Programming, Geometric Semantic Mutation, Particle Swarm Optimization, Semantics, Vector Fields",

author = "Palina Bartashevich and Sanaz Mostaghim and Illya Bakurov and Leonardo Vanneschi",

note = "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). New York: Association for Computing Machinery, Inc. DOI: 10.1145/3205651.3205760; 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 ; Conference date: 15-07-2018 Through 19-07-2018",

year = "2018",

month = jul,

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doi = "10.1145/3205651.3205760",

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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 et al.

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 proceeding › Conference contribution › peer-review

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T1 - Evolving PSO algorithm design in vector fields using geometric semantic GP

AU - Bartashevich, Palina

AU - Mostaghim, Sanaz

AU - Bakurov, Illya

AU - Vanneschi, Leonardo

N1 - 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). New York: Association for Computing Machinery, Inc. DOI: 10.1145/3205651.3205760

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N2 - 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.

AB - 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.

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KW - Semantics

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Evolving PSO algorithm design in vector fields using geometric semantic GP

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