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CFP: GECCO 2012 Genetics-based Machine Learning track deadline extended to 27 Jan. 2012

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Tim Kovacs

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8 janv. 2012, 22:05:3808/01/2012
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** The submission deadline for all tracks at GECCO 2012 has been
extended to January 27, 2012 **

====================================================================
GECCO 2012: Call for Papers on GENETICS-BASED MACHINE LEARNING (GBML)

2012 Genetic and Evolutionary Computation Conference (GECCO-2012)
The largest conference in the field of evolutionary computation
July 7-11, Philadelphia, USA
http://www.sigevo.org/gecco-2012/

**Extended submission deadline: January 27, 2012**

Co-located with the International Workshop on Learning Classifier
Systems (IWLCS)
====================================================================

The Genetics-Based Machine Learning (GBML) track at GECCO covers all
advances in theory and application of evolutionary computation methods
to Machine Learning (ML) problems.

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.

Evolutionary methods have a range of uses in ML:
- addressing subproblems of ML e.g.
- feature selection and construction
- optimising parameters of other ML methods
- as learning methods e.g.
- generating classification hypotheses with Genetic Programming
- learning control systems or cognitive modelling with Learning
Classifier Systems
- as meta-learners which adapt base learners e.g.
- evolving the structure and weights of neural networks
- evolving the data base and rule base in genetic fuzzy systems
- evolving ensembles of base learners
- evolving representations, update rules or algorithms for base
learners

The global search performed by evolutionary methods can complement the
local search of non-evolutionary methods and combinations of the two
are particularly welcome.

Free tutorials include:
- Learning Classifier Systems
- Large Scale Data Mining using Genetics-Based Machine Learning

Track Chairs

Dr. Will Browne, Victoria University of Wellington, NZ (will.browne -
at- ecs -dot- vuw -dot- ac -dot- nz)

Dr. Tim Kovacs, University of Bristol, U.K. (kovacs -at- cs -dot- bris
-dot- ac -dot- uk)

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