** Apologies for multiple postings **
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*** 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 ***
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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,[log in to unmask]
- Tim Kovacs,[log in to unmask]
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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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