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GECCO
2008
Springer
324views Optimization» more  GECCO 2008»
13 years 8 months ago
Convergence behavior of the fully informed particle swarm optimization algorithm
The fully informed particle swarm optimization algorithm (FIPS) is very sensitive to changes in the population topology. The velocity update rule used in FIPS considers all the ne...
Marco Antonio Montes de Oca, Thomas Stützle
GECCO
2006
Springer
178views Optimization» more  GECCO 2006»
13 years 11 months ago
Adaptively choosing niching parameters in a PSO
Niching techniques play an important role in evolutionary algorithms. Existing niching methods often require userspecified parameters, limiting their usefulness. This paper propos...
Stefan Bird, Xiaodong Li
BMCBI
2006
146views more  BMCBI 2006»
13 years 7 months ago
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
Michael Meissner, Michael Schmuker, Gisbert Schnei...
ISCI
2006
72views more  ISCI 2006»
13 years 7 months ago
A study of particle swarm optimization particle trajectories
Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO studies are empirical, with only a few theoretical analys...
F. Van den Bergh, Andries Petrus Engelbrecht
GECCO
2004
Springer
132views Optimization» more  GECCO 2004»
14 years 1 months ago
Grammatical Swarm
Abstract. This proof of concept study examines the possibility of specifying the construction of programs using a Particle Swarm algorithm, and represents a new form of automatic p...
Michael O'Neill, Anthony Brabazon