Publicação
Evolving PSO algorithm design in vector fields using geometric semantic GP
| Resumo: | 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. |
|---|---|
| Autores principais: | Bartashevich, Palina |
| Outros Autores: | Mostaghim, Sanaz; Bakurov, Illya; Vanneschi, Leonardo |
| Assunto: | EDDA Genetic Programming Geometric Semantic Mutation Particle Swarm Optimization Semantics Vector Fields Computer Science Applications Software Computational Theory and Mathematics Theoretical Computer Science |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | 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. |
|---|