Computer Science

Colloquium Details

Gravitational Interactions Optimization

Abstract

Evolutionary computation is inspired by nature in order to formulate metaheuristics capable to optimize several kinds of problems. A family of algorithms has emerged based on this idea; e.g. genetic algorithms, evolutionary strategies, particle swarm optimization (PSO), ant colony optimization (ACO), etc. In this talk I will present a population-based meta-heuristic inspired on the gravitational forces produced by the interaction of the masses of a set of bodies. My co-authors and I explored the physics knowledge in order to find useful analogies to design an optimization metaheuristic. The proposed algorithm is capable to find the optima of unimodal and multimodal functions commonly used to benchmark evolutionary algorithms. The proposed algorithm, Gravitational Interactions Optimization - GIO, has been compared with other meta-heuristics with respect to the mean number of evaluations needed to find the optima, outperforming several of them in many cases.

Biography

Juan J. Flores got a B.Sc. degree in Electrical Engineering from the Universidad Michoacana in 1984. In 1986 he got a M.Sc. degree in computer science from Centro de Investigacion y Estudios Avanzados, of the Instituto Politecnico Nacional. In 1997 got a Ph.D. degree in Computer Science from the University of Oregon. He is a full professor at the Universidad Michoacana since 1986. His research work deals with applications of Artificial Intelligence to Electrical Engineering, Information Security, and Financial Analysis. He is a member of the Sistema Nacional de Investigadores (National Researchers System), and the Mexican Academy of Sciences. He was a courtesy Faculty at the University of Oregon in 2005/2006. He is currently on sabbatical, at the University of Oregon.