Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
Llorà, X., Ács, B., Auvil, L., Capitanu, B., Welge, M.E., Goldberg, D.E. (2008)TR No.: 2008013 | Download PDF | Download PS
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 […]
Posted: October 24th, 2008 under Cloud computing. Comments: none
Evolutionary Computation in Conceptual Clustering and Tagging
Ueda, T. (2008)TR No.: 2008012 | Download PDF | Download PS
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 […]
Posted: May 19th, 2008 under Genetic algorithms. Comments: none
An Analysis of Matching in Learning Classifier Systems
Butz, M.V.,Lanzi, P.L., Llorà, X., Loiacono, D. (2008)TR No.: 2008011 | Download PDF | Download PS
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 […]
Posted: May 15th, 2008 under Genetic algorithms. Comments: none
Investigating Restricted Tournament Replacement in ECGA for Non-Stationary Environments
Lima,C.F., Fernandes, C., Lobo, F. G. (2008)TR No.: 2008010 | Download PDF | Download PS
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, […]
Posted: May 6th, 2008 under Genetic algorithms. Comments: none
Self-Adaptive Mutation in XCSF
Butz, M.V.,Stalph,P.,Lanzi, P.L. (2008)TR No.: 2008009 | Download PDF | Download PS
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 […]
Posted: April 29th, 2008 under Genetic algorithms. Comments: none
Real-Coded Extended Compact Genetic Algorithm based on Mixtures of Models
Lanzi,P.L,Nichetti,L., Sastry, K., Voltini,D., Goldberg, D. E. (2008)TR No.: 2008008 | Download PDF | Download PS
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 are manipulated as independent variables and estimates the population distribution using marginal product models (MPMs); […]
Posted: April 22nd, 2008 under Genetic algorithms. Comments: none
Sequential Problems that Challenge Generalization in Classifier Systems
Butz, M.V.,Lanzi, P.L. (2008)TR No.: 2008007 | Download PDF | Download PS
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 bounded sequential difficulty and bounded generalization difficulty. As an example, the approach is used here […]
Posted: April 22nd, 2008 under Genetic algorithms. Comments: none
Enhancing the Efficiency of the ECGA
Duque, T., Goldberg, D. E., Sastry, K. (2008)TR No.: 2008006 | Download PDF | Download PS
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 of two efficiency enhancements proposed for the Extended Compact Genetic Algorithm. First, a model building enhancement […]
Posted: April 3rd, 2008 under Genetic algorithms. Comments: none
Linkage Learning, Rule Representation, and the χ-ary Extended Compact Classifier System
Llorà, X., Sastry, K., Lima, C. F., Lobo, F. G., Goldberg, D. E. (2008)TR No.: 2008005 | Download PDF | Download PS
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 algorithm (GA) in the form of $\chi$-ary extended compact genetic algorithm, and (2) a niching method in […]
Posted: March 18th, 2008 under Genetic algorithms. Comments: none
Speeding Online Synthesis via Enforced Selecto-Recombination
Saruwatari, S., Llorà, X., Yasui, N. I., Tamura, H., Sastry, K., Goldberg, D. E (2008)TR No.: 2008004 | Download PDF | Download PS
Abstract: Brainstorming has been greatly used as a method to generate a large number of ideas by variety of each participant’s knowledge. However, brainstorming does not always work well because of spatial, communication limitations. Moreover, brainstorming techniques present limited scalability. Meanwhile, genetics algorithms have been mostly regarded as an engineering or technological tool. However, the innovation intuition suggests that […]
Posted: March 17th, 2008 under Genetic algorithms. Comments: none