Here are some blogs that I usually track. Sometimes they have fascinating stuff :D Apartment therapy: Now with NYC, LA, SF, and Chicago, also a Kitchen section! Big one! Tendir: A never-ending source of home decoration ideas. Grassroots modern: A small spinoff for affordable ideas. Funfurde: The first one I tracked, it opened my eyes. Archidose: A daily dose of architecture. The Design Weblog: Some interesting entry door to design.
Yup, it finally happen. I am back to Girona. Please allow me a slow response latency meanwhile :D
Well, it finally happened. GECCO started today at 8:30am. You can find live blogging at the IlliGAL Blogging.
by Xavier Llorà, Kumara Sastry , Francesc Alías, David E. Goldberg, and Michael Welge (2006). Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 1417–1418, ACM press. Also as IlliGAL TR No 2006004. Link to the PDF. Abstract This paper build on active interactive genetic algorithms and introduces visual-analytic techniques to aggregate, summarize, and visualize the information generated during interactive evolutionary processes. Special visualizations of the user-provided partial ordering of solutions, the synthetic fitness surrogates induced, and the model of user preferences were prepared. The visual-analytic techniques proposed point out potential pitfalls, strengths, and possible improvements in a non-trivial case study where the hierarchical tournament selection scheme of an active interactive genetic algorithm is replaced by an equivalent incremental selection scheme. Visual analytics provided an intuitive reasoning environment that unveiled important properties that greatly affect the performance of active interactive genetic algorithms that could not have been easily reveled otherwise. ...
by Xavier Llorà and Kumara Sastry (2006, accepted). Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 1513–1520, ACM press. Also as IlliGAL TR No 2006001. Link to the PDF. Abstract Over the last ten years XCS has become a de facto standard for Michigan-style learning classifier systems (LCS). Since the initial CS-1 work conceived by Holland, classifiers (rules) have widely used a ternary condition alphabet {0,1,#} for binary input problems. Most of the freely available implementations of this ternary alphabet in XCS rely on character-based encodings—easy to implement, not memory efficient, and expensive to compute. Profiling of freely available XCS implementations shows that most of their execution time is spent determining whether a rule is match or not, posing a serious thread to XCS scalability. In the last decade, multimedia and scientific applications have pushed CPU manufactures to include native support for vector instruction sets. This paper presents how to implement efficient condition encoding and fast rule matching strategies using vector instructions. The paper elaborates on Altivec (PowerPC G4, G5) and SSE2 (Intel P4/Xeon and AMD Opteron) instruction sets producing speedups of XCS matching process beyond ninety times. Moreover, such a vectorized matching code will allow to easily scale beyond tens of thousands of conditions in a reasonable time. The proposed fast matching scheme also fits in any other LCS other than XCS. ...