Daniele Codecasa

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The goal of this paper is to present four new parallel and distributed particle swarm optimization methods. and to experimentally compare their performances. These methods include a genetic algorithm whose individuals are co-evolving swarms, a different multi-swarm system and their respective variants enriched by adding a repulsive component to the(More)
Classification and clustering of streaming data are relevant in finance, computer science, and engineering while they are becoming increasingly important in medicine and biology. Streaming data are analyzed with algorithms and models capable to represent dynamics, sequences and time. Dynamic Bayesian networks and hidden Markov models are commonly used to(More)
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