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GECCO 2013 deadline extended to 31 Jan. 2013

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  • Jaume Bacardit
    ** Apologies for multiple postings ** ***** Submission for all GECCO 2013 tracks has been extended to ***** January 31, 2013 * CALL FOR PAPERS * 2013 GENETIC
    Message 1 of 1 , Jan 21, 2013
      ** Apologies for multiple postings **

      ***** Submission for all GECCO 2013 tracks has been extended to
      ***** January 31, 2013

      * Genetics-Based Machine Learning track
      * July 06-10, 2013, Amsterdam, The Netherlands
      * Organized by ACM SIGEVO
      * http://www.sigevo.org/gecco-2013 [www.sigevo.org]

      The Genetics-Based Machine Learning (GBML) track at GECCO 2013 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.

      The literature shows that evolutionary methods can tackle many
      different tasks within the ML context:

      - addressing subproblems of ML e.g. feature selection and
      - optimising parameters of other ML methods
      - as learning methods for classification, regression or control tasks
      - as meta-learners which adapt base learners
      * evolving the structure and weights of neural networks
      * evolving the data base and rule base in genetic fuzzy systems
      * evolving ensembles of 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.

      Some of the main GBML subfields are:

      * Learning Classifier Systems (LCS) are rule-based systems introduced
      by John Holland in the 1970s. LCSs are one of the most active and
      best-developed forms of GBML and we welcome all work on them.
      * Genetic Programming (GP) when applied to machine learning tasks (as
      opposed to function optimisation).
      * Evolutionary ensembles, in which evolution generates a set of
      learners which jointly solve problems.
      * Artificial Immune Systems (AIS).
      * Evolving neural networks or Neuroevolution.
      * Genetic Fuzzy Systems (GFS) which combine evolution and fuzzy

      In addition we encourage submissions including but not limited to the

      1. Theoretical advances

      * Theoretical analysis of mechanisms and systems
      * Identification and modeling of learning and scalability bounds
      * Connections and combinations with machine learning theory
      * Analysis and robustness in stochastic, noisy, or
      non-stationary environments
      * Complexity analysis in MDP and POMDP problems
      * Efficient algorithms

      2. Modification of algorithms and new algorithms

      * Evolutionary rule learning, including but not limited to:
      o Michigan style (SCS, NewBoole, EpiCS, ZCS, XCS, UCS...)
      o Pittsburgh style (GABIL, GIL, COGIN, REGAL, GA-Miner, GALE,
      MOLCS, GAssist...)
      o Anticipatory LCS (ACS, ACS2, XACS, YACS, MACS...)
      o Iterative Rule Learning Approach (SIA, HIDER, NAX,
      * Artificial Immune Systems
      * Genetic fuzzy systems
      * Learning using evolutionary Estimation of Distribution
      Algorithms (EDAs)
      * Evolution of Neural Networks
      * Evolution of ensemble systems
      * Other hybrids combining evolutionary techniques with other
      machine learning techniques

      3. Issues in GBML

      * Competent operator design and implementation
      * Encapsulation and niching techniques
      * Hierarchical architectures
      * Default hierarchies
      * Knowledge representations, extraction and inference
      * Data sampling
      * (Sub-)Structure (building block) identification and linkage
      * Integration of other machine learning techniques
      * Mechanisms to improve scalability

      4. Applications

      * Data mining
      * Bioinformatics and life sciences
      * Rapid application development frameworks for GBML
      * Robotics, engineering, hardware/software design, and control
      * Cognitive systems and cognitive modeling
      * Dynamic environments, time series and sequence learning
      * Artificial Life
      * Adaptive behavior
      * Economic modelling
      * Network security
      * Other kinds of real-world applications

      5. Related Activities
      * Visualisation of all aspects of GBML (performance, final
      solutions, evolution of the population)
      * Platforms for GBML, e.g. GPGPUs
      * Competitive performance, e.g. GBML performance in Competitions
      and Awards
      * Education and dissemination of GBML, e.g. software for
      teaching and exploring aspects of GBML.

      All accepted papers will appear in the proceedings of GECCO 2013,
      which will be published by ACM (Association for Computing Machinery).

      Important Dates:

      January 31, 2013 - Extended (and final) paper submission deadline
      April 17, 2013 - Camera-ready version of accepted articles
      July 06-10, 2013 - GECCO 2013 Conference in Amsterdam

      Track Chairs:
      - Jaume Bacardit,jaume.bacardit@...
      - Tim Kovacs,kovacs@...

      Jaume Bacardit, PhD
      Lecturer in Bioinformatics
      University of Nottingham

      Interdisciplinary Computing and Complex Systems Research Group,
      School of Computer Science, Jubilee Campus, Nottingham, NG8 1BB, UK
      URL: http://icos.cs.nott.ac.uk
      Twitter: @ICO2S

      Tel: +441158467044
      Fax: +441159516292
      Email: jaume _dot_ bacardit _at_ nottingham _dot_ ac _dot_ uk
      Web: http://www.cs.nott.ac.uk/~jqb
      Twitter: @jaumebp

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