Technical Reports

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 (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. ...