Polynomial Modeling in a Dynamic Environment based on a Particle Swarm Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{chan:2012:cia,

• author = "Kit Yan Chan and Tharam S. Dillon",
• title = "Polynomial Modeling in a Dynamic Environment based on a Particle Swarm Optimization",
• booktitle = "Computational Intelligence and Its Applications",
• publisher = "World Scientific",
• year = "2012",
• editor = "H K Lam and Steve S H Ling and Hung T Nguyen",
• pages = "23--38",
• keywords = "genetic algorithms, genetic programming, particle swarm optimisation, PSO, time-varying modelling, time-varying systems, polynomial modelling, evolutionary computation",
• isbn13 = "978-1-84816-691-2",
• DOI = "doi:10.1142/9781848166929_0002",
• bibsource = "OAI-PMH server at espace.library.curtin.edu.au",
• oai = "oai:espace.library.curtin.edu.au:189166",
• URL = "http://espace.library.curtin.edu.au/R?func=dbin-jump-full&local_base=gen01-era02&object_id=189166",
• abstract = "In this chapter, a particle swarm optimisation (PSO) is proposed for polynomial modelling in a dynamic environment. The basic operations of the proposed PSO are identical to the ones of the original PSO except that elements of particles represent arithmetic operations and polynomial variables of polynomial models. The performance of the proposed PSO is evaluated by polynomial modelling based on a set of dynamic benchmark functions in which their optima are dynamically moved. Results show that the proposed PSO can find significantly better polynomial models than genetic programming (GP) which is a commonly used method for polynomial modelling.",

}

Genetic Programming entries for Kit Yan Chan Tharam S Dillon

Citations