LCS – LCS & GBML Central

GECCO 2011: Genetics-Based Machine Learning Track Announcement and CFP

GBML call for papers for GECCO 2011

Genetic and Evolutionary Computation Conference (GECCO) is one of the most prestigious double-blind peer review conference in Evolutionary Computation. Based on its impact factor, GECCO is 11th in the rankings of 701 international conferences in artificial intelligence, machine learning, robotics, and human-computer interactions. During 2011, GECCO will take place in the beautiful city of Dublin, Ireland between the 12th and 16th of July.

Guinness Storehouse - The social event will take place at Ireland’s No. 1 international visitor attraction

GECCO 2011: Call for Papers on Genetics-Based Machine Learning (GBML)

Deadline: January 26, 2011

2011 Genetic and Evolutionary Computation Conference (GECCO-2011)

July 12-16, Dublin, Ireland

The Genetics-Based Machine Learning (GBML) track encompasses advancements and new developments in any system that addresses machine learning problems with evolutionary computation methods. Combinations of machine learning with evolutionary computation techniques are particularly welcome.

Machine Learning (ML) presents an array of paradigms — unsupervised, semi-supervised, supervised, and reinforcement learning — which frame a wide range of clustering, classification, regression, prediction and control tasks. The combination of the global search capabilities of Evolutionary Computation with the reinforcement abilities of ML underlies these problem solving tools.

The field of Learning Classifier Systems (LCS), introduced by John Holland in the 1970s, is one of the most active and best-developed forms of GBML and we welcome all work on LCSs. Artificial Immune Systems (AIS) are another family of techniques included in this track, which takes inspiration of different immunological mechanisms in vertebrates in order to solve computational problems. Moreover, neuroevolution technologies, which combine neural network techniques with evolutionary computation, are welcome. However, also any other related technique or approach will be considered gladly. See the list of suggested (but not limited to) topics at:

http://www.sigevo.org/gecco-2011/organizers-tracks.html#gbml

For more information on GECCO 2011 visit:

http://www.sigevo.org/gecco-2011/.

Sincerely,

Track Organizers

Dr. Will Browne, Victoria University of Wellington, NZ (will.browne@vuw.ac.nz)

Dr. Ester Bernadó-Mansilla, La Salle – Ramon Llull University, Barcelona,

Spain (esterb@salle.url.edu)

Trinity_College_Front_Square — Visit many of Dublin's interesting and historic places

GECCO is sponsored by the Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). SIG Services: 2 Penn Plaza, Suite 701, New York, NY, 10121, USA, 1-800-342-6626 (USA and Canada) or +212-626-0500 (Global).

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

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…

LCS and Software Development

“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. Related posts:NIGEL 2006 Part II: Dasgupta vs. Booker Large Scale Data Mining using Genetics-Based Machine Learning Software for fast rule matching using vector instructions

“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.

GAssist and GALE Now Available in Python

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 […]

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

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 […]

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.

Large Scale Data Mining using Genetics-Based Machine Learning

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 […]

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.

NIGEL 2006 Part VI: Bacardit

After coming back from GECCO I just uploaded the last of the NIGEL 2006 talks at LCS & GBML Central. This last talk was by Jaume Bacardit and GBML for protein structure prediction.

Related posts:NIGEL 2006 Part V: Bernardó vs. LanziNIGEL 2006 Part IV: Llorà vs. CasillasNIGEL 2006 Part III: Butz vs. Barry

After coming back from GECCO I just uploaded the last of the NIGEL 2006 talks at LCS & GBML Central. This last talk was by Jaume Bacardit and GBML for protein structure prediction.

NIGEL 2006 revisited (Part VI): Bacardit

This is the last of the NIGEL talks NIGEL 2006 talks. Enjoy this last one

Jaume Bacardit

Video
[vimeo clip_id=5065758 width=”432″ height=”320″]

Slides
[slideshare id=1384657&doc=nigel-2006-bacardit-090504154202-phpapp02]

NIGEL 2006 Part V: Bernardó vs. Lanzi

After the vacation break, two more NIGEL 2006 talks are available at LCS & GBML Central. This week Ester Bernardó presents how LCS can perform in the presence of class imbalance, whereas Lanzi continues his quest on computed predictions.

Related posts:NIGEL 2006 Part IV: Llorà vs. CasillasTranscoding NIGEL 2006 videosNIGEL 2006 Part III: Butz […]

NIGEL 2006 revisited (Part V): Bernadó and Lanzi

After a brief break, the two last rounds of talks are coming. This week two more NIGEL 2006 talks. Enjoy this fifth release, Bernadó vs. Lanzi.

Ester Bernardó-Mansilla

Video
[vimeo clip_id=5065762 width=”432″ height=”320″]

Slides
[slideshare id=1384643&doc=nigel-2006-bernado-090504153926-phpapp02]

Pier Luca Lanzi

Video
[vimeo clip_id=5065667 width=”432″ height=”320″]

Slides
[slideshare id=1384584&doc=nigel-2006-lanzi-090504152951-phpapp02]