A short python script with methods to allow the user to easily generate n-mulitplexer problem data. Users can either generate single instances, datasets with a specified number of random instances, or complete datasets of all unique multiplexer instances (memory allowing) to be saved in a .txt file.
Author: docurbs
Python Code for EK_AF_UCS_2.0 Now Available
We have organized, annotated, and cleaned up the code for our published Michigan-Style Learning Classifier System implementations. EK_AF_UCS stands for Expert Knowledge and Attribute Feedback Supervised Classifier System. The above code was utilized in the following publication:
- Tan, J., Moore, JH., Urbanowicz, R. Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System. Advances in Artificial Life, ECAL. Vol. 12, 110-117, 2013
Python Code for AF_UCS_2.0 with Multicore Parallelization Now Available
AF_UCS_2.0_Multicore_Parallelization
We have organized, annotated, and cleaned up the code for our published Michigan-Style Learning Classifier System implementations. AF_UCS stands for Attribute Feedback Supervised Classifier System. The above code was utilized in the following publications:
- Rudd, J., Moore, JH., Urbanowicz, R. A simple multi-core parallelization strategy for learning classifier system evaluation. Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion. ACM. 1259-1266, 2013
- Rudd, J., Moore, JH., Urbanowicz, R. A multi-core parallelization strategy for statistical significance testing in learning classifier systems. Evolutionary Intelligence. {in press} 2013
Python Code for EK_AF_UCS_1.0 Now Available
We have organized, annotated, and cleaned up the code for our published Michigan-Style Learning Classifier System implementations. EK_AF_UCS stands for Expert Knowledge and Attribute Feedback Supervised Classifier System. The above code was utilized in the following publications:
- Urbanowicz, R., Andrew, A., Karagas, M., Moore, J. Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach. Journal of the American Medical Informatics Association. 20:4, 603-612, 2013
- Urbanowicz, R., Granizo-Mackenzie, D., Moore, J. Using expert knowledge to guide covering and mutation in a michigan style learning classifier system to detect epistasis and heterogeneity. Parallel Problem Solving from Nature-PPSN XII. Springer. 266-275, 2012
Python Code for AF_UCS_1.0 Now Available
We have organized, annotated, and cleaned up the code for our published Michigan-Style Learning Classifier System implementations. AF_UCS stands for Attribute Feedback Supervised Classifier System. The above code was utilized in the following publications:
- Urbanowicz, R., Granizo-Mackenzie, A., Moore, J. An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems. Computational Intelligence Magazine, IEEE. 35-45, 2012
- Urbanowicz, R., Granizo Mackenzie, A., Moore, J. Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems. Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference. ACM. 927-934, 2012
Deadline approaching: IWLCS @ GECCO (March 28)
Just a quick reminder that the deadline for the IWLCS will be here in two weeks (March 28). IWLCS is a great place to present your quality projects and ongoing work related to LCS research. This year it is particularly important for you to make a contribution to this workshop which serves as a valuable and central exchange of knowledge and ideas for those interested in the study of these unique algorithms, as well as for those interested in learning more about them.
Last year the IWLCS only got two paper submissions, a record low for the meeting. It is our hope that we will see many more submissions this year in order to demonstrate interest in this workshop.
Submission instructions are on the IWLCS 2013 website under the CFP tab.
http://homepages.ecs.vuw.ac.nz/~iqbal/iwlcs2013/index.html
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** CALL FOR PAPERS **
** Sixteenth International Workshop on Learning Classifier Systems **
** July 06-10, 2013, Amsterdam, The Netherlands **
** Organized by ACM SIGEVO **
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The Sixteenth International Workshop on Learning Classifier Systems (IWLCS
2013) will be held in Amsterdam, The Netherlands during the Genetic and
Evolutionary Computation Conference (GECCO-2013), July 06-10, 2013.
Originally, Learning Classifier Systems (LCSs) were introduced by John H.
Holland as a way of applying evolutionary computation to machine learning
and adaptive behaviour problems. Since then, the LCS paradigm has
broadened greatly into a framework that encompasses many representations,
rule discovery mechanisms, and credit assignment schemes.
Current LCS applications range from data mining, to automated innovation
and the on-line control of cognitive systems. LCS research includes
various actual system approaches: While Wilson’s accuracy-based XCS system
(1995) has received the highest attention and gained the highest
reputation; studies and developments of other LCSs are usually discussed
and contrasted. Advances in machine learning, and reinforcement learning
in particular, as well as in evolutionary computation have brought LCS
systems the necessary competence and guaranteed learning properties. Novel
insights in machine learning and evolutionary computation are being
integrated into the LCS framework.
Thus, we invite submissions that discuss recent developments in all areas
of research on, and applications of, Learning Classifier Systems. IWLCS is
the event that brings together most of the core researchers in classifier
systems. The workshop also provides an opportunity for researchers
interested in LCSs to get an impression of the current research directions
in the field as well as a guideline for the application of LCSs to their
problem domain.
Topics of interests include but are not limited to:
Paradigms of LCS (Michigan, Pittsburgh …)
Theoretical developments (behaviour, scalability and learning bounds …)
Representations (binary, real-valued, oblique, non-linear, fuzzy …)
Types of target problems (single-step, multiple-step, regression/function
approximation …)
System enhancements (competent operators, problem structure identification
and linkage learning …)
LCS for Cognitive Control (architectures, emergent behaviours …)
Applications (data mining, medical domains, bioinformatics …)
Optimizations and parallel implementations (GPU, matching algorithms …)
All accepted papers will be presented at IWLCS 2013 and will appear in the
GECCO workshop volume, which will be published by ACM (Association for
Computing Machinery). Authors will be invited after the workshop to submit
revised (full) papers that, after a thorough review process, are to be
published in a special issue of the Evolutionary Intelligence journal.
Important dates
March 28, 2013 – Paper submission deadline
April 15, 2013 – Notification to authors
April 25, 2013 – Submission of camera-ready material
July 06-10, 2013 – GECCO 2013 Conference in Amsterdam, The Netherlands
Organizing Committee
Muhammad Iqbal, muhammad.iqbal@ecs.vuw.ac.nz
Kamran Shafi, k.shafi@adfa.edu.au
Ryan Urbanowicz, ryan.j.urbanowicz@dartmouth.edu
Regards
Ryan Urbanowicz
Python LCS Implementations (GALE & GAssist) for SNP Environment
The above .zip files contain open source python implementations of existing LCS algorithms (GALE & GAssist) written/modified to accommodate SNP (single nucleotide polymorphism) gene association studies. These are the implementations used in the following paper recently accepted at PPSN:
R.J. Urbanowicz, J.H Moore. The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis
in Association Studies. PPSN 2010
Python LCS Implementations (XCS, UCS, MCS) for SNP Environment
The above .zip files contain open source python implementations of existing LCS algorithms (XCS, UCS, MCS) written/modified to accommodate SNP gene association studies. These are the implementations used in the following paper published in the proceeding of GECCO 2009:
R.J. Urbanowicz, J.H Moore. The Application of Michigan-Style Learning Classifier
Systems to Address Genetic Heterogeneity and Epistasis
in Association Studies. GECCO 2010
An LCS Review for Beginners and Non-Computer Scientists.
I am pleased to share with you that the Journal of Artificial Evolution and Applications has recently published my LCS Review paper entitled, “Learning Classifier Systems: A Complete Introduction, Review, and Roadmap”. I wrote this from the perspective of a non-computer scientist, to introduce the basic LCS concept, as well as the variation represented in different LCS implementations that have been tasked to different problem domains. It was my goal and hope that this review might provide a reasonable starting point for outsiders interested in understanding or getting involved in the LCS community. This paper may be viewed using the following link: Thanks! I enjoyed listening to the many excellent GBML talks given at GECCO this year.