No, this is not about the art of placing fences around your garden; this is about fencing, the sport. Jose L. Balcázar introduced Javier, Hector, and me to fencing during his visit to UIUC in July 2003. He is a fencer at SAM (Sala d’Armes de Montjuïc) in Barcelona. Since then, I am a member of The Point Fencing Club in Champaign and join the United States Fencing Association to be able to take part in the Illinois division tournaments. Here hare some useful links ...
This is the presentation for the initial paper on the compact classifier system published in CEC 2005 (Edimburgh., Scotland, September 2005). The presentation slides here (1Mb).
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. ...
by Llorà, X., Sastry, K., Goldberg, D.E., Gupta, A., Lakshmi, L. (2005). Published in the ACM Genetic and Evolutionary Computation Conference (GECCO 2005), ACM press, pp. 1363–1371. Also as IlliGAL TR No 2005009. Link to the PDF Abstract: One of the daunting challenges of interactive genetic algorithms (iGAs)—genetic algorithms in which fitness measure of a solution is provided by a human rather than by a fitness function, model, or computation—is user fatigue which leads to sub-optimal solutions. This paper proposes a method to combat user fatigue by augmenting user evaluations with a synthetic fitness function. The proposed method combines partial ordering concepts, notion of non-domination from multiobjective optimization, and support vector machines to synthesize a fitness model based on user evaluation. The proposed method is used in an iGA on a simple test problem and the results demonstrate that the method actively combats user fatigue by requiring 3–7 times less user evaluation when compared to a simple iGA. ...
Keynote speaker talk in Congreso Mexicano de Computación Evolutiva (CONCEV-2005) about how evolutionary models and chance discovery can support human innovation and creativity. The presentation may be download here (PDF 8.5Mb).