Remembering Leo Breiman

Leo Breiman was a highly creative, influential researcher with a down-to-earth personal style and an insistence on working on important real world problems and producing useful solutions. This paper is a short review of Breiman's extensive contributions to the field of applied statistics.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS427 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Educational Simulator for Teaching of Particle Swarm Optimization in LabVIEW

This paper presents an educational software tool for aid the teaching of Particle Swarm Optimization (PSO) fundamentals with friendly design interface. This software were developed in the platform of LabVIEW (Laboratory Virtual Intrumentation Engineering Workbench). The software‟s best qualities are users can select many different version of the PSO algorithm, a lot of the benchmarks test functions for optimization and set the parameters that have an influence on the PSO performance. Through visualization of particle distribution in the searching, the simulator is particularly effective in providing users with an intuitive feel for the PSO algorithm

Solving one-dimensional unconstrained global optimization problem using parameter free filled function method

It is generally known that almost all filled function methods for one-dimensional unconstrained global optimization problems have computational weaknesses. This paper introduces a relatively new parameter free filled function, which creates a non-ascending bridge from any local isolated minimizer to other first local isolated minimizer with lower or equal function value. The algorithm’s unprecedented function can be used to determine all extreme and inflection points between the two considered consecutive local isolated minimizers. The proposed method never fails to carry out its job. The results of the several testing examples have shown the capability and efficiency of this algorithm while at the same time, proving that the computational weaknesses of the filled function methods can be overcomed