Publications

by Xavier Llorà (2006). IlliGAL TR No 2006017. Link to the PDF. Link to the Java code Abstract This technical report describes how to compute the fitness of a rule for an arbitrary size multiplexer without doing any instance matching. Pittsburgh-style learning classifier systems require the accuracy and the error of a rule to compute a fitness that promotes maximally accurate and maximally general rules. The accuracy (α) may be computed as the proportion of overall examples correctly classified, and the error (ε) is the proportion of incorrect classifications issued. Once the accuracy and error of a rule are known, the fitness can be computed as f(r)=α(r)*ε(r). This technical note shows how to computed the fitness only by inspecting the rule, requiring a time proportional to number of possible address values O(2^|a|) instead of the O(2^l) that requires a traditional rule matching strategy against all the possible instances. The proposed method makes tractable for Pittsburgh learning classifier systems multiplexer problems larger than the 11-input one. ...

by Xavier Llorà and David E. Goldberg, Yukio Ohsawa, Naohiro Matsumura, Yuichi Washida, Hiroshi Tamura, Masataka Yoshikawa, Michael Welge, Loretta Auvil, Duane Searshmith, Kei Ohnishi, and Chen-Ju Chao (2006). New Mathematics and Natural Computation, World Scientific, pp. 2(1):85–100. Link to the Journal publication. Abstract Creativity protocols and methodologies tend to be time consuming if applied manually. This paper presents how information technologies can support innovation and creativity for collaborative scenario creation and discussion. The fusion of change discovery, genetics algorithms, and computer-supported collaborative tools provide computational models of innovation and creativity. The proposed technology allows groups of participants in a creative processes to have pervasive access to the analysis of the current scenario in real time. This paper introduces such innovation technologies gathered in the DISCUS project, and summarizes initial successful usages of DISCUS on marketing research workshops. ...

by Xavier Llorà and David E. Goldberg (2006). IlliGAL TR No 2006011. Link to the PDF. Abstract The pervasive expansion of computers and Internet has change the way people collaborate. Terms such as cybercollaboratories are getting traction in day-to-day work. Web boards, blogs, e-mails, and instant messaging have become de facto mainstream communication channels. People scattered across the globe collaborate thanks to such technologies to carry out their daily work. Creative processes—such as collaborative engineering—have also taken advantage of such new communication media. This paper reviews the new framework set after these technologies and presents how collaborative creativity and innovation can be modeled and supported using computational models. The paper continues presenting a innovation-support model based on the usage of genetic algorithms as computational metaphors of human innovation. The paper also briefly discuses the results achieved using the proposed technologies in real-world collaborative creative processes. ...

by Francesc Alías, Xavier Llorà, Lluís Formiga, Kumara Sastry, and David E. Goldberg (2006, accepted). IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2006). May 14-19, 2006. Also as IlliGAL TR No 2005022. Link to the PDF. Abstract Active interactive genetic algorithms (aiGAs) rely on actively optimizing synthetic fitness functions. In interactive genetic algorithms (iGAs) framework, user evaluations provide the necessary input for synthesizing a reasonably accurate surrogate fitness function that models user evaluations or, in other words, his/her decision preferences. User evaluations collected via tournament selection only provide partial-ordering relations between solutions. Active iGAs assemble a partial-ordering graph of user evaluations. In such a directed graph, any contradictory evaluation provided by the user introduces a cycle in the graph. This property is explored in this paper to measure the consistency of the evaluations provided by the user along the evolutionary process. The consistency measures are applied to a real-world problem, the weight tuning of the cost function involved in corpus-based text-to-speech synthesis. Results show the usefulness of such measures to identify inconsistent users during the evolutionary tuning process, and successfully the number of evaluations required by more than half. ...

by Xavier Llorà, Kumara Sastry, and David E. Goldberg (2006). Proceedings of the Congress on Evolutionary Computation, 1, 596—603. Also as IlliGAL TR No 2005019. Link to the PDF. Abstract This paper presents an analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style classifier system. In order to be able to perform such an analysis we introduce a simple bare-bones Pittsburgh-style classifier systems—the compact classifier system (CCS)—based on estimation of distribution algorithms. Using a common rule encoding schemes of Pittsburgh-style classifier systems, CCS mantains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of the initially perturbated probability vectors. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally general and accurate rules. The initial theoretical analysis and results also show that traditional encoding schemes used by Pittsburgh-style classifiers add an extra facet of diffiiculty. Such a bias plays a central role on the overall performance and scalability of CCS and other Pittsburgh-style systems using such encoding schemes. ...