Understanding Particle Swarm Optimisation by Evolving Problem Landscapes

Created by W.Langdon from gp-bibliography.bib Revision:1.8194

@InProceedings{langdon:2005:SIS,

• author = "W. B. Langdon and Riccardo Poli and Owen Holland and Thiemo Krink",
• title = "Understanding Particle Swarm Optimisation by Evolving Problem Landscapes",
• booktitle = "Proceedings SIS 2005 IEEE Swarm Intelligence",
• year = "2005",
• editor = "Luca Maria Gambardella and Payman Arabshahi and Alcherio Martinoli",
• pages = "30--37",
• address = "Pasadena, California, USA",
• month = "8-10 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, XPS",
• ISBN = "0-7803-8917-4",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2005_SIS.pdf",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2005_SIS.ps.gz",
• DOI = "doi:10.1109/SIS.2005.1501599",
• size = "8 pages",
• abstract = "Genetic programming (GP) is used to create fitness landscapes which highlight strengths and weaknesses of different types of PSO and to contrast population-based swarm approaches with non stochastic gradient followers (i.e. hill climbers). These automatically generated benchmark problems yield insights into the operation of PSOs, illustrate benefits and drawbacks of different population sizes and constriction (friction) coefficients, and reveal new swarm phenomena such as deception and the exploration/exploitation tradeoff. The method could be applied to any type of optimizer.",

}

Genetic Programming entries for William B Langdon Riccardo Poli Owen Holland Thiemo Krink

Citations