Resumen de A tutorial for Stochastic Global Optimisation Algorithms

Eligius Hendrix

• Stochastic GO Algorithms aim at generating good solutions in reasonable time. The aim of the contribution is to come to a didactical introduction for non-mathematic students of about 20 pages into the concepts of region of attraction, population ideas, e ectiveness and eciency determination, design of experiments etc. The idea is to elaborate small examples a student can elaborate by hand, such that the concepts are grasped. In one page text with an example the ideas of: Sampling in high dimensional space, Multistart, Clustering, Pure Adaptive Search, Hit and Run and SA, Population variants like Controlled Random Search, Raspberries, GA, DE, Particle Swarms etc. are elaborated.

  We stress that e ectiveness is hard to measure and requires systematic evaluation and analysis. Numerical comparison requires simple benchmark algorithms, clear performance indicator de nition and the principle that any other researchers should be able to repeat the experiment. A avour is given in the presentation.