I returned from Brussels a couple of days ago, where I went to present KANTS: Artificial Ant System for Classification (the model was already described here) at the 6th International Conference on Ant Colony Optimization and Swarm Intelligence. ANTS 2008 is similar to PPSN, with most of the papers being presented at poster sessions (only a few are chosen for oral presentation). This procedure works well when the poster sessions are not just a minor event of the congress, thrown to a distant room in the hotel/university where nobody even bothers to go, or scheduled to the end of a long day. ANTS sessions were well organized and every poster had an assigned space. My presentation was scheduled to the last day of the congress, when most of people had already packed for their trip back home, but nonetheless the session went well, with lots of people wandering around the room, clearly interested in the works. KANTS got the attention of some audience, and I think they were quite impressed by the simplicity (and efficiency) of the idea. The inevitable question arouse (are you planning to test KANTS on a real-world problem?) and this time we can answer yes, we are not only planning to do it, but we are already working on it (later we will report on those experiments). In the same section, another swarm-clustering was presented. I saw the poster, and the results on clustering appear to be quite good (but the system does not perform classification). I haven’t read the paper (as a matter of fact, it is published as an extended abstract), but I was able to realize that the algorithm is little bit complex, simulating the behavior of three different species: ants, birds and spiders.
A week before I was in Barcelona, at the 8th International Conference on Hybrid Intelligent Systems (HIS 2008), presenting the paper Tracking Extrema in Dynamic Fitness Functions with Dissortative Mating Genetic Algorithms. It is quite a different work, and more related to my thesis’ subject, bio-inspired computation on dynamic environments. It describes the experiments performed with an adaptive dissortative mating GA (ADMGA) on Dynamic Optimization Problems. Dissortative mating appears frequently in nature and refers to the occurrence of mating between dissimilar individuals more often than expected by chance. It maintains genetic diversity at a higher level, thus increasing the exploration stage of the algorithm. Dynamic fitness functions are more sensible to genetic diversity than static ones, and so dissortative mating is a good candidate to deal with that kind of problems. The paper describes mainly the experiments performed with trap functions and show that ADMGA may improve GAs on some dynamic optimization scenarios. Robustness is also addressed and results show that ADMGA maintains a more stable performance over the wide range of dynamic scenarios. The congress HIS is mainly dedicated to hybrid models and real-world applications, so ADMGA was somewhat “lost” among other works. But good news came after the congress, and this line of research will probably make its way through other media.
P.S. In Brussels, avoid Hotel Continental, near Midi Station, unless you need inspiration for another insects’ heuristics.
GeNeura Team 