Category Archives: Technical reports

Abstract: Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. … Continue reading →

Abstract:Data-intensive flow computing allows efficient processing of large volumes of data otherwise unapproachable. This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust and transparent scalable solution from a laptop to large-scale clusters,(2) creates an unified … Continue reading →

Abstract: The Web 2.0 technologies provide users with collaborative work-spaces over the Internet. For example, Wikipedia is an open source encyclopedia that anyone can edit articles. YouTube provides spaces where users can share videos and annotations about them. Users can put … Continue reading →

Abstract: We investigate rule matching in learning classifier systems for problems involving binary and real inputs. We consider three rule encodings: the widely used character-based encoding, a specificity-based encoding, and a binary encoding used in Alecsys. We compare the performance of … Continue reading →

Abstract: This paper investigates the incorporation of restricted tournament replacement (RTR) in the extended compact genetic algorithm (ECGA) for solving problems with non-stationary optima. RTR is a simple yet efficient niching method used to maintain diversity in a population of … Continue reading →

Abstract: Recent advances in XCS technology have shown that self-adaptive mutation can be highly useful to speed-up the evolutionary progress in XCS. Moreover, recent publications have shown that XCS can also be successfully applied to challenging real-valued domains including datamining, function … Continue reading →

Abstract: This paper presents a real-coded estimation distribution algorithm (EDA) inspired to the extended compact genetic algorithm (ECGA) and the real-coded Bayesian Optimization Algorithm (rBOA). Like ECGA, the proposed algorithm partitions the problem variables into a set of clusters that … Continue reading →

We present an approach to build sequential decision making problems which can challenge the generalization capabilities of classifier systems. The approach can be applied to any sequential problem defined over a binary domain and it generates a new problem with … Continue reading →

Abstract: Evolutionary Algorithms are largely used search and optimization procedures that, when properly designed, can solve intractable problems in tractable polynomial time. Efficiency enhancements are used to turn them from tractable to practical. In this paper we show preliminary results … Continue reading →

Abstract: This paper reviews a competent Pittsburgh LCS that automatically mines important substructures of the underlying problems and takes problems that were intractable with  first-generation Pittsburgh LCS and renders them tractable. Specifically, we propose a χ-ary extended compact classifier system  which uses (1) a competent genetic … Continue reading →