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CFP: Genetics-Based Machine Learning track at GECCO-2013

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  • Jaume Bacardit
    ** Apologies for multiple postings ** ***************************************************************************** *** CALL FOR
    Message 1 of 2 , Dec 6, 2012
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      ** Apologies for multiple postings **

      *****************************************************************************
      *** CALL FOR PAPERS ***
      *** 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2013) ***
      *** Genetics-Based Machine Learning track ***
      *** July 06-10, 2013, Amsterdam, The Netherlands ***
      *** Organized by ACM SIGEVO ***
      ***http://www.sigevo.org/gecco-2013 ***
      *****************************************************************************

      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 construction
      - 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 logic.

      In addition we encourage submissions including but not limited to the
      following:

      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, BioHEL,...)
      * 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 learning
      * 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 23, 2013 - Paper submission deadline
      April 17, 2013 - Camera-ready version of accepted articles
      July 06-10, 2013 - GECCO 2013 Conference in Amsterdam, The Netherlands


      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
      --------------------------------------------------------------------

      This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please send it back to me, and immediately delete it. Please do not use, copy or disclose the information contained in this message or in any attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham.

      This message has been checked for viruses but the contents of an attachment
      may still contain software viruses which could damage your computer system:
      you are advised to perform your own checks. Email communications with the
      University of Nottingham may be monitored as permitted by UK legislation.
    • Jaume Bacardit
      ** Apologies for multiple postings ** ****************************************************************** CALL FOR PAPERS 2013 GENETIC AND EVOLUTIONARY
      Message 2 of 2 , Jan 10, 2013
      • 0 Attachment
        ** Apologies for multiple postings **

        ******************************************************************
        CALL FOR PAPERS
        2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2013)
        Genetics-Based Machine Learning track
        July 06-10, 2013, Amsterdam, The Netherlands
        Organized by ACM SIGEVO
        http://www.sigevo.org/gecco-2013
        ******************************************************************

        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
        construction
        - 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
        logic.

        In addition we encourage submissions including but not limited to the
        following:

        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,
        BioHEL,...)
        * 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
        learning
        * 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 23, 2013 - Paper submission deadline
        April 17, 2013 - Camera-ready version of accepted articles
        July 06-10, 2013 - GECCO 2013 Conference in Amsterdam, The Netherlands


        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
        --------------------------------------------------------------------

        This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please send it back to me, and immediately delete it. Please do not use, copy or disclose the information contained in this message or in any attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham.

        This message has been checked for viruses but the contents of an attachment
        may still contain software viruses which could damage your computer system:
        you are advised to perform your own checks. Email communications with the
        University of Nottingham may be monitored as permitted by UK legislation.
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