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Evolving PSO algorithm design in vector fields using geometric semantic GP
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Orientador(es)
Resumo(s)
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.
Descrição
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
Palavras-chave
EDDA Genetic Programming Geometric Semantic Mutation Particle Swarm Optimization Semantics Vector Fields Computer Science Applications Software Computational Theory and Mathematics Theoretical Computer Science
Contexto Educativo
Citação
Editora
ACM - Association for Computing Machinery