GBML track at GECCO-2012

GECCO-2012 features the Genetics-Based Machine Learning (GBML) track, which 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
Dr. Tim Kovacs, University of Bristol, U.K.

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