Introducing particle swarm optimization into a genetic algorithm to evolve robot controllers

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Langosz:2014:GECCOcomp,

• author = "Malte Langosz and Kai Alexander {von Szadkowski} and Frank Kirchner",
• title = "Introducing particle swarm optimization into a genetic algorithm to evolve robot controllers",
• booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion",
• year = "2014",
• editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges",
• isbn13 = "978-1-4503-2881-4",
• keywords = "genetic algorithms, genetic programming, ant colony optimization and swarm intelligence: Poster",
• pages = "9--10",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "http://doi.acm.org/10.1145/2598394.2598474",
• DOI = "doi:10.1145/2598394.2598474",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "This paper presents Swarm-Assisted Behaviour Graph Evolution (SABRE), a genetic algorithm which combines elements from genetic programming and neuroevolution to develop Behaviour Graphs (BGs). SABRE evolves graph structure and parameters in parallel, with particle swarm optimisation (PSO) being used for the latter. The algorithm's performance was evaluated on a set of black-box function approximation problems, one of which represents part of a robot controller. We found that SABRE performed significantly better in approximating the mathematically complex test functions than the reference algorithms genetic programming (GP) and NEAT.",
• notes = "Also known as \cite{2598474} Distributed at GECCO-2014.",

}

Genetic Programming entries for Malte Langosz Kai Alexander von Szadkowski Frank Kirchner

Citations