Xavier Llorà » Learning Classifier Systems

IWLCS 2010 – Discussion session on LCS / XCS(F)

Xavier — Mon, 21 Jun 2010 22:04:08 +0000

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.

So, I am sure you all have some issues in mind that you think should be tackled / addressed / discussed at the workshop and in the near future.

Thus, I would be very happy to receive a few suggestions from your side – anything is welcome – I will then compile the points raised in a few slides to try and get the discussion going at the workshop.

Thank you for any feedback you can provide.

Looking forward to seeing you soon!

Martin

P.S.: Please feel free to also forward this message or tell me, if you think this Email should be still sent to other people…
—-

PD Dr. Martin V. Butz <butz@psychologie.uni-wuerzburg.de>

Department of Psychology III (Cognitive Psychology)
Roentgenring 11
97070 Wuerzburg, Germany
http://www.coboslab.psychologie.uni-wuerzburg.de/people/martin_v_butz/
http://www.coboslab.psychologie.uni-wuerzburg.de
Phone: +49 (0)931 31 82808
Fax: +49 (0)931 31 82815

GAssist and GALE Now Available in Python

Xavier — Fri, 11 Jun 2010 17:06:35 +0000

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.

More information about Ryan’s implementations can found below

Side note: my original GALE implementation can also be downloaded here.

LCS & GBML Central Gets a New Home

Xavier — Fri, 04 Jun 2010 04:32:38 +0000

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. Both functionalities, rarely used in their current form, have been replaced by a simpler public embedded Wave reachable at http://gbml.org/wave. The goal, provide people in the LCS & GBML community a simpler way to discuss, share, and hang out.

About the feeds being aggregated, I have revised the list and added the feeds now available of the table of contents from

I have also added a few other links to relevant research groups doing work on related areas. Please, leave a comment on this post if you know/have a related site that could be aggregated, or if there are missing links to research groups or useful resources.

GECCO 2010 Submission Deadline (Extended)

Xavier — Sat, 19 Dec 2009 12:11:34 +0000

If you are planning to submit a paper for the 2010 Genetic and Evolutionary Computation Conference, the deadline is January 13, 2010 (and now extended to January 27th). You can find more information at the GECCO 2010 calendar site. Related posts:GECCO 2009 paper submission deadline extended till January 28 GECCO 2007 deadline extended GECCO 2009 [...] Related posts:

Large Scale Data Mining using Genetics-Based Machine Learning

Xavier — Wed, 15 Jul 2009 21:56:17 +0000

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. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them.

This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.

Transcoding NIGEL 2006 videos

Xavier — Mon, 04 May 2009 20:13:23 +0000

Last week Pier Luca Lanzi was visiting IlliGAL. Yesterday, before he left for Chicago, we went for one last brunch. He mentioned that he liked a lot the videos we shot during NIGEL 2006. Thinking about it we agreed would be useful to recover the videos and upload them into some of the usual video sharing site suspects. Currently they are hosted, for long term storage purposes, at NCSA’s web archive. I spent sometime retrieving them from the archive (they are pretty fat and encoded in wmv) and I stated transcoding it in m4a. My plan? Make them available via Vimeo and LCS & GBML Central. Also, I will be uploading the presentation slides to SlideShare and also make them available via LCS & GBML Central.

Update: The first two videos (Wilson and Goldberg) are already available at LCS & GBML Central.

Deadline extended for special issue on Metaheuristics for Large Scale Data Mining

Xavier — Tue, 07 Apr 2009 15:32:03 +0000

The deadline for the special issue on Metaheuristics for Large Scale Data Mining to be published by Springer’s Memetic Computing Journal has been extended till May 31, 2009. More information can be found in this post at LCS & GBML Central.

LCSweb + GBML blog = LCS & GBML Central

Xavier — Fri, 27 Mar 2009 16:27:02 +0000

LCSweb was designed to allow researchers and those seeking to use Learning Classifier Systems within applications access to material on LCS and discussion between members of the LCS community. The site served this community since its was started by Alwyn Barry in 1997. Enhanced and maintained later by Jan Drugowitsch, LCSweb became a valuable community resource. The site was completely community-driven and allowed members to contribute to the content of the site and keeping it up to date. Later on in 2005, I started “LCS and other GBML” Blog to cover a gap providing information information regarding the International Workshop on Learning Classifier Systems (IWLCS), the collection of LCS Books available, and GBML related news.

Some of you may have realized that after Jan’s move to Rochester and Alwyn’s retirement from research activities, LCSweb has vanished. Will Browne took on himself to take LCSweb to Reading, but technical circumstances have made that move rocky despite his best efforts. Jan and Will however still have a local copy of LCSweb contents. After talking to Jan and Will, I proposed to merge LCSweb with the LCS and other GBML blog, and host the new site at NCSA where dedicated resources has been made available. Jan and Will agreed with the idea.

We are happy to announce that the merged site (still under the update cycle) can be reached at http://lcs-gbml.ncsa.uiuc.edu. More information about the process can be found here or at there LCS & GBML Central site.

Dusting my Ph.D. thesis off

Xavier — Tue, 30 Dec 2008 10:11:00 +0000

After attending Albert Orriols’s Ph.D. thesis defense, I ended wondering how many of the question I posted in mine have not been solved. The answer, quite a bit. So, I just decided to dig it up, and put it up here. Yes, the thesis was not written in English (in those days my fellowship had some strings attached), but math formulation, graphs, and results are readable in any language Also, GALE was written and documented in english, and is available here.

Abstract:
This thesis combines different ideas provided by machine learning, evolutionary computation (genetics-based machine learning), and artificial life. The aim of this work is to create a data mining model that satisfies certain guidelines. The first one deals with the type of attributes that can be used in the model. The second one focuses on the goal that the model has to independent of the knowledge representation used. Finally, the model must exploit massive parallelism. Artificial Life can be very useful dealing with parallelism models.This thesis combines different ideas provided by machine learning, evolutionary computation (genetics-based machine learning), and artificial life. The aim of this work is to create a data mining model that satisfies certain guidelines. The first one deals with the type of attributes thatcan be used in the model. The second one focuses on the goal that the model has to independent of the knowledge representation used. Finally, the model must exploit massive parallelism. Artificial Life can be very useful dealing with parallelism models.

Scribd

Join me congratulating Albert Orriols, Ph.D.

Xavier — Fri, 12 Dec 2008 23:35:30 +0000

Albert Orriols (for those who do not know him, a brilliant learning classifier systems researcher) defended his thesis today. The outcome: Excellent Cum Laude. Albert Orriols Ph.D. defense started at 11am at Enginyeria i Arquitectura La Salle (Universitat Ramon Llull) in Barcelona. The thesis panel was presided by Prof. David E. Goldberg, and formed by members Prof. Francisco Herrera, myself, Dr. Martin Butz, and secretary Prof. Xavier Vilasís). I must say that it has been a great pleasure to read his remarkable thesis and great contributions to the Learning Classifier System field. I hope he will make it available soon, and encourage you to take a look at it.