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In this seminar I will present a new hybrid approach to numerical optimization which combines Particle Swarm Optimization (PSO) and Estimation of Distribution Algorithms (EDAs). PSO is a popular optimization technique based on swarm intelligence concepts, while EDAs are a relatively new class of evolutionary algorithms which build a probabilistic model of the population dynamics and use this model to sample new individuals. The algorithm PSEDA, proposed in this work, is based on a mixture of Gaussian distributions. PSEDA can be seen as an implementation of the PSO behaviour in the EDAs framework, where estimation of probability distributions is used to drive the particles dynamics, i.e. the movement of particles in the search space.

Valentino Santucci is a 3rd year PhD student in Computer Science and Mathematics at University of Perugia (Italy) and it's now an exchange PhD at Department of Computer Science of Hong Kong Baptist University. His research field is Evolutionary Computation, a subfield of Artificial Intelligence that exploits concepts from biological evolution and collective intelligence in order to solve hard optimization problems. He has received a M.S. in Computer Science with a final mark of 110/110 cum laude and he is a member of the Italian Association for Artificial Intelligence (AIxIA).