IWLCS 2011: 14th International Workshop on Learning Classifier Systems

14TH INTERNATIONAL WORKSHOP ON LEARNING CLASSIFIER SYSTEMS
to be held as part of the

2011 Genetic and Evolutionary Computation Conference (GECCO-2011)
July 12-16, Dublin, Ireland

Organized by ACM SIGEVO
20th International Conference on Genetic Algorithms (ICGA) and the 16th
Annual Genetic Programming Conference (GP)

One Conference – Many Mini-Conferences 15 Program Tracks

PAPER SUBMISSION DEADLINE FOR WORKSHOP: April 7th, 2011

http://home.dei.polimi.it/loiacono/iwlcs2011
Continue reading “IWLCS 2011: 14th International Workshop on Learning Classifier Systems”

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
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)

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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).

Reinforcement Learning, Logic and Evolutionary Computation

Drew Mellor is pleased to announce the publication of his new LCS book. Reinforcement Learning, Logic and Evolutionary Computation: A Learning Classifier System Approach to Relational Reinforcement Learning, published by Lambert Academic Publishing (ISBN 978-3-8383-0196-9).

Abstract Reinforcement learning (RL) consists of methods that automatically adjust behaviour based on numerical rewards and penalties. While use of the attribute-value framework is widespread in RL, it has limited expressive power. Logic languages, such as first-order logic, provide a more expressive framework, and their use in RL has led to the field of relational RL. This thesis develops a system for relational RL based on learning classifier systems (LCS). In brief, the system generates, evolves, and evaluates a population of condition-action rules, which take the form of definite clauses over first-order logic. Adopting the LCS approach allows the resulting system to integrate several desirable qualities: model-free and “tabula rasa” learning; a Markov Decision Process problem model; and importantly, support for variables as a principal mechanism for generalisation. The utility of variables is demonstrated by the system’s ability to learn genuinely scalable behaviour – ! behaviour learnt in small environments that translates to arbitrarily large versions of the environment without the need for retraining.

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 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]

NIGEL 2006 revisited (Part IV): Llorà and Casillas

This week two more NIGEL 2006 talks. Enjoy this third release, Llorà vs. Casillas.

Xavier Llorà

Video
[vimeo clip_id=4727857 width=”432″ height=”320″]

Slides
[slideshare id=1384570&doc=nigel-2006-llora-xeccs-090504152642-phpapp01]

Jorge Casillas

Video
[vimeo clip_id=4727943 width=”432″ height=”320″]

Slides
[slideshare id=1550779&doc=nigel-2006-casillas-090608160722-phpapp02]

NIGEL 2006 revisited (Part III): Butz and Barry

This week two more NIGEL 2006 talks. Enjoy this third release, Butz vs. Barry.

Martin Butz

Video
[vimeo clip_id=4593358 width=”432″ height=”320″]

Slides
[slideshare id=1384628&doc=nigel-2006-butz-090504153553-phpapp02]

Alwyn Barry

Video
[vimeo clip_id=4727803 width=”432″ height=”320″]

Slides
[slideshare id=1384652&doc=nigel-2006-barry-090504154054-phpapp01]

NIGEL 2006 revisited (Part II): Booker and Dasgupta

This week two more NIGEL 2006 talks. Enjoy this second release, Dasgupta vs. Booker.

Dipankar Dasgupta

Video
[vimeo clip_id=4592273 width=”432″ height=”320″]

Slides
[slideshare id=1384601&doc=nigel-2006-dasgupta-090504153353-phpapp01]

Lashon Booker

Video
[vimeo clip_id=4592087 width=”432″ height=”320″]

Slides
[slideshare id=1384637&doc=nigel-2006-booker-090504153739-phpapp02]

NIGEL 2006 revisited (Part I): Wilson and Goldberg

I finally finished transcoding the videos from NIGEL 2006 and started uploading them to Vimeo. Every week I will upload two of them following NIGEL 2006 agenda. I will also embed the slides that are already available on SlideShare, if available for the talk. Enjoy this first release, Wilson vs. Goldberg. I have also included the meeting introduction just for nostalgia purposes.

Introduction

Video
[vimeo clip_id=4479633 width=”432″ height=”320″]

Slides
[slideshare id=1384574&doc=nigel-2006-llora-welcome-090504152827-phpapp02]

Stewart Wilson

Video
[vimeo clip_id=4478921 width=”432″ height=”320″]

Slides
Unfortunately, Stewart’s slides are not available.

David E. Goldberg

Video
[vimeo clip_id=4477260 width=”432″ height=”320″]

Slides
[slideshare id=1384594&doc=nigel-2006-goldberg-090504153108-phpapp02]

Memetic Computing Journal special issue on Metaheuristics for Large Scale Data Mining – Extended Deadline

Aim and Scope

Data mining and knowledge discovery are crucial techniques across many scientific disciplines. Recent developments such as the Genome Project (and its successors) or the construction of the Large Hadron Collider have provided the scientific community with vast amounts of data. Metaheuristics and other evolutionary algorithms have been successfully applied to a large variety of data mining tasks. Competitive metaheuristic approaches are able to deal with rule, tree and prototype induction, neural networks synthesis, fuzzy logic learning, and kernel machines–to mention but a few. Moreover, the inherent parallel nature of some metaheuristics (e.g. evolutionary approaches, particle swarms, ant colonies, etc) makes them perfect candidates for approaching very large-scale data mining problems.

Although a number of recent techniques have applied these methods to complex data mining domains, we are still far from having a deep and principled understanding of how to scale them to datasets of terascale, petascale or even larger scale. In order to achieve and maintain a relevant role in large scale data mining, metaheuristics need, among other features, to have the capacity of processing vast amounts of data in a reasonable time frame, to use efficiently the unprecedented computer power available nowadays due to advances in high performance computing and to produce when possible- human understandable outputs.

Several research topics impinge on the applicability of metaheuristics for data mining techniques: (1) proper scalable learning paradigms and knowledge representations, (2) better understanding of the relationship between the learning paradigms/representations and the nature of the problems to be solved, (3) efficiency enhancement techniques, and (4) visualization tools that expose as much insight as possible to the domain experts based on the learned knowledge.

We would like to invite researchers to submit contributions on the area of large-scale data mining using metaheuristics. Potentially viable research themes are:

  • Learning paradigms based on metaheuristics, evolutionary algorithms, learning classifier systems, particle swarm, ant colonies, tabu search, simulated annealing, etc
  • Hybridization with other kinds of machine learning techniques including exact and approximation algorithms
  • Knowledge representations for large-scale data mining
  • Advanced techniques for enhanced prediction (classification, regression/function approximation, clustering, etc.) when dealing with large data sets
  • Efficiency enhancement techniques
  • Parallelization techniques
  • Hardware acceleration techniques (vectorial instuctions, GPUs, etc.)
  • Theoretical models of the scalability limits of the learning paradigms/representations
  • Principled methodologies for experiment design (choosing methods, adjusting parameters, etc.)
  • Explanatory power and visualization of generated solutions
  • Data complexity analysis and measures
  • Ensemble methods
  • Online data mining and data streams
  • Examples of real-world successful applications

Instructions for authors

Papers should have approximately 20 pages (but certainly not more than 24 pages). The papers must follow the format of the Memetic Computing journal:
http://www.springer.com/engineering/journal/12293?detailsPage=contentItemPage&CIPageCounter=151543

Papers should be submitted following the Memetic Computing journal guidelines. When submitting the paper please select this special issue as the article type.

Important dates

  • Manuscript submission: May 31st, 2009
  • Notification of acceptance: July 31st, 2009
  • Submission of camera-ready version: Sep 30th, 2009

Guest editors:

Jaume Bacardit
School of Computer Science and School of Biosciences
University of Nottingham
jaume.bacardit@nottingham.ac.uk

Xavier Llorà
National Center for Supercomputing Applications
University of Illinois at Urbana-Champaign
xllora@illinois.edu