Several constrained and unconstrained optimization problems have been adequately solved over the years thanks to advances in the metaheuristics area. In the last decades, different metaheuristics have been proposed employing new ideas, and hybrid algorithms that improve the original metaheuristics have been developed. One of the most successfully employed metaheuristics is the Differential Evolution. In
This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to
In this work we present an approach to extract and to structure bibliographical references from BibTex files, allowing the identification of the duplicate ones, which can appear slightly different in different files. To deal with this problem, existing systems use classifiers, clustering or others algorithms, allied with an Edit Distance metric, to distinguish between duplicate
We propose a multi-view DE in which several mutation strategies are applied to the same current population to generate different views for the current iteration. The views are then merged using tournament-selection to generate the next single population. This Multi-View DE is benchmarked on the BBOB-2012 noiseless function testbed. Keywords: procés de test.