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 on a wide set of benchmark functions. These methods include a genetic algorithm whose individuals are co-evolving swarms, an "island model"-based multi-swarm system, where swarms are independent and they(More)
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)
We present four new parallel and distributed particle swarm optimization methods consisting in a genetic algorithm whose individuals are co-evolving swarms, an “island model”-based multi-swarm system, where swarms are independent and interact by means of particle migrations at regular time steps, and their respective variants enriched by adding a repulsive(More)
Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. New continuous time Bayesian network classifiers are introduced while their conditional log-likelihood scoring function is developed. A learning algorithm, combining conditional log-likelihood with Bayesian parameter(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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