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Evolutionary computation: optimization and inference

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Abstract: Evolutionary computation is a general class of computational tools, which draw their inspiration from nature. The most famous evolutionary algorithm is the Genetic Algorithm (GA), firstly introduced by Holland in the early 1970s. These kinds of algorithms are usually based on population of individuals that are iteratively evolved through an objective, and they are widely used to tackle combinatorial optimization problems. The aim of the seminar is to give an overview on evolutionary techniques, mainly focusing on GA, and their application to systems biology. I will also present an application of integration of evolutionary algorithms and stochastic modelling and simulation (using BlenX language) to infer reaction rates of a metabolic pathway where just part of the experimental data are available.

Biography: Michele Forlin obtained a Bachelor’s degree (2003) and a Master’s degree (2006) in Statistics at the University Ca’ Foscari of Venice. His tesis focused on the problem of experimental design (DoE) for high dimensional biochemical experimentation using an information theoretical approach. In 2006/2007 he attended the Second level International Master in Computational and Systems Biology at the Microsoft Research – University of Trento Centre for Computational and Systems Biology. From 2005 to 2007 he collaborated with the University of Venice Group at the Department of Statistics on the European Project Programmable Artificial Cell Evolution (PACE).

This talk is part of the CoSBi Computational and Systems Biology Series series.

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