Author: will

** Apologies for multiple postings **

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** CALL FOR PAPERS                                                                                             **
** 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE   (GECCO-2014) **
** Evolutionary Machine Learning track                                                             **
** July 12-16, 2014, Vancouver, BC, Canada                                                       **
** Organized by ACM SIGEVO                                                                               **
** http://www.sigevo.org/gecco-2014                                                                  **
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You are invited to plan your participation in the Genetic and
Evolutionary Computation Conference (GECCO 2014). This conference will
present the latest high-quality results in genetic and evolutionary
computation.

** Important dates **

* Abstract submission: January 15, 2014
* Submission of full papers: January 29, 2014
* Notification of paper acceptance: March 12, 2014
* Camera ready submission: April 14, 2014
* Conference: July 12-16, 2014

** Call for Papers: Evolutionary Machine Learning Track **

[New incarnation of GBML-track]

http://www.sigevo.org/gecco-2014/organizers-tracks.html#eml

The Evolutionary Machine Learning (EML) 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.

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 ML methods, a.k.a. hyper-parameter tuning
– 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 EML 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 EML and we welcome all work on them.
– Hyper-parameter tuning with evolutionary methods.
– 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.
– Evolving neural networks or Neuroevolution when applied to ML tasks.

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
– Efficient algorithms

2. Modification of algorithms and new algorithms
– Evolutionary rule learning, including but not limited to:
* Michigan style (SCS, NewBoole, EpiCS, ZCS, XCS, UCS…)
* Pittsburgh style (GABIL, GIL, COGIN, REGAL, GA-Miner, GALE, MOLCS,
GAssist…)
* Anticipatory LCS (ACS, ACS2, XACS, YACS, MACS…)
* Iterative Rule Learning Approach (SIA, HIDER, NAX, BioHEL, …)
– Genetic fuzzy systems
– Evolution of Neural Networks
– Evolution of ensemble systems
– Other hybrids combining evolutionary techniques with other machine
learning techniques

3. Issues in EML
– Competent operator design and implementation
– Encapsulation and niching techniques
– Hierarchical architectures
– (Sub-)Structure (building block) identification and linkage learning
– Knowledge representations, extraction and inference
– Data sampling
– Scalability

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

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

** Submissions **

Abstracts need to be submitted by January 15, 2014. Full papers are due
by the **__non-extensible deadline__** of January 29, 2014. Detailed
submission instructions can be found at http://www.sigevo.org/gecco-2014.

Each paper submitted to GECCO will be rigorously evaluated in a
double-blind review process. The evaluation is on a per-track basis,
ensuring high interest and expertise of the reviewers. Review criteria
include significance of the work, technical soundness, novelty, clarity,
writing quality, and sufficiency of information to permit replication,
if applicable. All accepted papers will be published in the ACM Digital
Library.

GECCO allows submission of material that is substantially similar to a
paper being submitted contemporaneously for review by another
conference. However, if the submitted paper is accepted by GECCO, the
authors agree that substantially the same material will not be published
by another conference. Material may later be revised and submitted to a
journal, if permitted by the journal.

Researchers are invited to submit abstracts of their work recently
published in top-tier conferences and journals to the new Hot Off the
Press track. Contributions will be selected based on quality and
interest to the GECCO community.

** Track Chairs **

– Jaume Bacardit, jaume.bacardit@newcastle.ac.uk
– Tom Schaul, schaul@gmail.com