Learning Classifier Systems

I just got an email from Martin Butz about a discussion session being planned for IWLCS 2010 and his request to pass it along. Hope all is well and you are going to attend GECCO this year. Regardless if you attend or not: Jaume asked me to lead a discussion session on “LCS representations, operators, and scalability – what is next?” … or similar during IWLCS… Basically everything besides datamining, because there will be another session on that topic....

“On the Road to Competence” is a slide deck by Jurgen Appelo with interesting analogies between learning classifier systems and software development. Definitely worth taking a look at it.

Ryan Urbanowicz has released Python versions of GAssits and GALE!!! Yup, so excited to see a new incarnation of GALE doing the rounds. I cannot wait to get my hands on it. Ryan has also done an excellent job porting UCS, XCS, and MCS to Python and making those implementations available via “LCS & GBML central” for people to use. I think Ryan’s efforts deserve recognition. His code is helping others to have an easier entry to the LCS and GBML....

Today I finished migrating the LCS & GBML Central site from its original URL (http://lcs-gbml.ncsa.uiuc.edu) to a more permanent and stable home located at http://gbml.org. The original site is already currently redirecting the trafic to the new site, and it will be doing so for a while to help people transition and update bookmarks and feed readers. I have introduced a few changes to the functionality of the original site. Functional changes can be mostly summarized by (1) dropping the forums section and (2) closing comments on posts and pages....

Below you may find the slides of the GECCO 2009 tutorial that Jaume Bacardit and I put together. Hope you enjoy it. Slides Abstract We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling....