Category Archives: Virtual library

An LCS Review for Beginners and Non-Computer Scientists.

I am pleased to share with you that the Journal of Artificial Evolution and Applications has recently published my LCS Review paper entitled, “Learning Classifier Systems: A Complete Introduction, Review, and Roadmap”. I wrote this from the perspective of a … Continue reading

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Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre

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. Two representative cases—selectorecombinative genetic algorithms and estimation of distribution algorithms—are presented, analyzed, discussed. This study […] Continue reading

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Meandre: Semantic-Driven Data-Intensive Flows in the Clouds

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 solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and […] Continue reading

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Evolutionary Computation in Conceptual Clustering and Tagging

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 images on Flickers and collaborate each other by categorizing with tagging. These contents are created […] Continue reading

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An Analysis of Matching in Learning Classifier Systems

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 the three algorithms both on matching alone and on typical test problems. The results on […] Continue reading

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Investigating Restricted Tournament Replacement in ECGA for Non-Stationary Environments

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 individuals. While the original version of RTR uses Hamming distance to quantify similarity between individuals, […] Continue reading

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Self-Adaptive Mutation in XCSF

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 approximation, and clustering. In this paper, we combine these two advances and investigate self-adaptive mutation […] Continue reading

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Classifier Fitness Based on Accuracy

In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier’s fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, … Continue reading

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