CFP: IEEE CEC 2017 Special Session: Genetics-Based Machine Learning to Evolutionary Machine Learning

Dear Colleagues,

 

We would like to invite you to submit a paper for the Special Session on Genetics-Based Machine Learning to Evolutionary Machine Learning at 2017 IEEE Congress on Evolutionary Computation (CEC 2017), which will be held in Donostia – San Sebastián, Spain,  June 5-8, 2017. If you are interested in our special session and planning to submit a paper, please let us know beforehand. We would like to have a list of tentative papers. Of course, you can submit it without the reply to this message.

 

Special Session:  Genetics-Based Machine Learning to Evolutionary Machine Learning

Organizers: Masaya Nakata, Yusuke Nojima, Will Browne, Keiki Takadama, Tim Kovacs

 

Evolutionary Machine Learning (EML) explores technologies that integrate machine learning with evolutionary computation for tasks including optimization, classification, regression, and clustering. Since machine learning contributes to a local search while evolutionary computation contributes to a global search, one of the fundamental interests in EML is a management of interactions between learning and evolution to produce a system performance that cannot be achieved by either of these approaches alone.

 

Historically, this research area was called genetics-based machine learning (GBML) and it was concerned with learning classifier systems (LCS) with its numerous implementations such as fuzzy learning classifier systems (Fuzzy LCS). More recently, EML has emerged as a more general field than GBML; EML covers a wider range of machine learning adapted methods such as genetic programming for ML, evolving ensembles, evolving neural networks, and genetic fuzzy systems; in short, any combination of evolution and machine learning. EML is consequently a broader, more flexible and more capable paradigm than GBML.

 

From this viewpoint, the aim of this special session is to explore potential EML technologies and clarify new directions for EML to show its prospects. This special session is the third edition of our previous special sessions in CEC2015 and CEC2016. The continuous exploration of this field by organizing the special session in CEC is indispensable to establish the discipline of EML.

– Evolutionary learning systems (e.g., learning classifier systems)

– Evolutionary fuzzy systems

– Evolutionary data mining

– Evolutionary reinforcement learning

– Evolutionary neural networks

– Evolutionary adaptive systems

– Artificial immune systems

– Genetic programming applied to machine learning

– Evolutionary feature selection and construction for machine learning

– Transfer learning; learning blocks of knowledge (memes, code, etc.) and evolving the sharing to related problem domains

– Accuracy-Interpretability trade-off in EML

– Applications and theory of EML

 

Important dates are as follows:

– Paper Submission Deadline: January 16, 2017

– Paper Acceptance Notification: February 26, 2017

– Final Paper Submission Deadline: TBD

– Conference Dates: June 5-8, 2017

 

Further information about the special session and the conference can be found at:

– 2017 IEEE Congress on Evolutionary Computation

http://www.cec2017.org/#special_session_sessions

– Special Session on http://www.cec2017.org

https://sites.google.com/site/cec2017ssndeml/home

 

Best regards,

Masaya, Yusuke, Will, Keiki, Tim

FINAL submission deadline for IEEE WCCI 2016

IEEE WCCI 2016 has been extended till

31st January 2016, 24:00 EST

Special Session on New Directions in Evolutionary Machine Learning

2016 IEEE Congress on Evolutionary Computation (WCCI2016/CEC2016 )

Vancouver, Canada, 25-29 July, 2016

[See previous post below for details of the call for papers for the special session most suited to Genetics-based Machine Learning and Learning Classifier Systems]

Please select the special session under the main research topic (otherwise the paper will be treated as a general paper and may be reviewed by researchers outside of this field):

7be New Directions in Evolutionary Machine Learning

Special Session on New Directions in Evolutionary Machine Learning at WCCI/CEC 2016

Dear LCS, GBML, RBML and EML Researcher,

Apologies for the multiple postings as WCCI/CEC has now approved the Special Sessions.

Please forward this CFP to your colleagues, students, and those who may be interested. Thank you.

Call for Papers

Special Session on New Directions in Evolutionary Machine Learning

2016 IEEE Congress on Evolutionary Computation (WCCI2016/CEC2016 )

Vancouver, Canada, 25-29 July, 2016

Aim and scope:

Evolutionary Machine Learning (EML) explores technologies that integrate machine learning with evolutionary computation for tasks including optimization, classification, regression, and clustering. Since machine learning contributes to a local search while evolutionary computation contributes to a global search, one of the fundamental interests in EML is a management of interactions between learning and evolution to produce a system performance that cannot be achieved by either of these approaches alone.

Historically, this research area was called GBML (genetics-based machine learning) and it was concerned with learning classifier systems (LCS) with its numerous implementations such as fuzzy learning classifier systems (Fuzzy LCS).

Recently, EML has emerged as a more general field than GBML; EML covers a wider range of machine learning adapted methods such as genetic programming for ML, evolving ensembles, evolving neural networks, and genetic fuzzy systems; in short, any combination of evolution and machine learning. EML is consequently a broader, more flexible and more capable paradigm than GBML. From this viewpoint, the aim of this special session is to explore potential EML technologies and clarify new directions for EML to show its prospects.

This special session follows the first successful special session (the largest session among the special sessions) held in CEC 2015. The continuous exploration of this field by organizing the special session in CEC is indispensable to establish the discipline of EML. For this purpose, this special session focuses on, but is not limited to, the following areas in EML:

– Evolutionary learning systems (e.g., learning classifier systems)

– Evolutionary fuzzy systems

– Evolutionary reinforcement learning

– Evolutionary neural networks

– Evolutionary adaptive systems

– Artificial immune systems

– Genetic programming applied to machine learning

– Transfer learning; learning blocks of knowledge (memes, code, etc.) and evolving the sharing to related problem domains

– Accuracy-Interpretability tradeoff in EML

– Applications and theory of EML

 

Organisers:

Will Browne (*1), Keiki Takadama (*2), Yusuke Nojima (*3), Masaya Nakata (*4), Tim Kovacs (*5)

E-mail:

(*1) will.browne@vuw.ac.nz, (*2) keiki@inf.uec.ac.jp, (*3) nojima@cs.osakafu-u.ac.jp,

(*4) m.nakata@cas.hc.uec.ac.jp (*5) tim.kovacs@bristol.ac.uk

Affiliations:

(*1) Victoria University of Wellington, New Zealand

(*2) The University of Electro-Communications, Japan

(*3) Osaka Prefecture University, Japan

(*4) The University of Electro-Communications, Japan

(*5) University of Bristol, UK

 

Associated Website:

https://sites.google.com/site/wcci2016sseml/

 

John H. Holland

Sad news that John Holland passed away on the weekend. A warm obituary can be found here:

http://www.santafe.edu/news/item/in-memoriam-john-holland/

Many people’s lives and research have been touched by his ideas and enthusiasm.  This site definitely would not exist without them.

Curiously, his passing may not have been major mainstream news, but his ideas are. It was interesting to note that fields with his ideas were name checked in the latest Google announcement: https://abc.xyz.

History will undoubtedly recognise John as a pioneer in the computer age.

 

 

 

CEC Deadline extension

Important Dates

Paper submission deadline: December 19, 2014 January 16, 2015 (Extended)

Paper acceptance notification: February 20, 2015

Final paper submission deadline: March 13, 2015

Conference dates: May 25-28, 2015

Special Session on

New Directions in Evolutionary Machine Learning

Motivation

Evolutionary Machine Learning (EML) explores technologies that integrate machine learning with evolutionary computation for tasks including optimization, classification, regression, and clustering. Since machine learning contributes to a local search while evolutionary computation contributes to a global search, one of the fundamental interests in EML is a management of interactions between learning and evolution to produce a system performance that cannot be achieved by either of these approaches alone. Historically, this research area was called GBML (genetics-based machine learning) and it was concerned with learning classifier systems (LCS) with its numerous implementations such as fuzzy learning classifier systems (Fuzzy LCS). More recently, EML has emerged as a more general field than GBML; EML covers a wider range of machine learning adapted methods such as genetic programming for ML, evolving ensembles, evolving neural networks, and genetic fuzzy systems; in short, any combination of evolution and machine learning. EML is consequently a broader, more flexible and more capable paradigm than GBML. From this viewpoint, the aim of this special session is to explore potential EML technologies and clarify new directions for EML to show its prospects. For this purpose, this special session focuses on, but is not limited to, the following areas in EML:

– Evolutionary learning systems (e.g., learning classifier systems) – Evolutionary fuzzy systems

– Evolutionary data mining - Evolutionary reinforcement learning - Evolutionary neural networks

– Evolutionary adaptive systems       – Artificial immune systems - Accuracy-Interpretability tradeoff in EML

– Applications and theory of EML – Genetic programming applied to machine learning

– Evolutionary feature selection and construction for machine learning - Transfer learning; learning blocks of knowledge (memes, code, etc.) and evolving the sharing to related problem domains

Important Dates

Paper submission deadline: December 19, 2014 January 16, 2015 (Extended)

Paper acceptance notification: February 20, 2015

Final paper submission deadline: March 13, 2015

Conference dates: May 25-28, 2015

Paper Submission

Special session papers are treated the same as regular papers and must be submitted via the CEC 2015 submission website. To submit your paper to this special session, you have to choose our special session (ID SS52) on the submission page.

Organizers

  • Keiki Takadama, The University of Electro-Communications, Japan (Contact: keiki@inf.uec.ac.jp)
  • Tim Kovacs, University of Bristol, UK.
  • Yusuke Nojima, Osaka Prefecture University, Japan
  • Will Browne, Victoria University of Wellington, New Zealand
  • Masaya, Nakata, The University of Electro-Communications, Japan

 

Special Session URL: https://sites.google.com/site/cec2015sseml/

Conference URL: http://sites.ieee.org/cec2015/

New Directions in Evolutionary Machine Learning at 2015 IEEE Congress on Evolutionary Computation (CEC 2015)

Call to submit a paper for the special session on New Directions in Evolutionary Machine Learning at 2015 IEEE Congress on Evolutionary Computation (CEC 2015) which will be held in Sendai, Japan at May 25-28, 2015.
If you are interested in our special session and planing to submit a paper, please let us know beforehand. We would like to have a list of tentative papers. Of course, you can submit it without the reply to this message. Please choose the session ID SS52 on the submission system.

Special Session: New Directions in Evolutionary Machine Learning
Organizers: Keiki Takadama, Tim Kovacs, Yusuke Nojima, Will Browne, Masaya Nakata

Evolutionary Machine Learning (EML) explores technologies that integrate machine learning with evolutionary computation for tasks including optimization, classification, regression, and clustering. Since machine learning contributes to a local search while evolutionary computationcontributes to a global search, one of the fundamental interests in EML is a management of interactions between learning and evolution to produce a system performance that cannot be achieved by either of these approaches alone. Historically, this research area was called GBML (genetics-based machine learning) and it was concerned with learning classifier systems (LCS) with its numerous implementations such as fuzzy learning classifier systems(Fuzzy LCS). More recently, EML has emerged as a more general field than GBML; EML covers a wider range of machine learning adapted methods such as genetic programming for ML, evolving ensembles, evolving neural networks, and genetic fuzzy systems; in short, any combination of evolution and machine learning. EML is consequently a broader, more flexible and more capable paradigm than GBML. From this viewpoint, the aim of this special session is to explore potential EML technologies and clarify new directions for EML to show its prospects. For this purpose, this special session focuses on, but is not limited to, the following areas in EML:

– Evolutionary learning systems (e.g., learning classifier systems)
– Evolutionary fuzzy systems
– Evolutionary data mining
– Evolutionary reinforcement learning
– Evolutionary neural networks
– Evolutionary adaptive systems colleagues,
– Artificial immune systems
– Genetic programming applied to machine learning
– Evolutionary feature selection and construction for machine learning
– Transfer learning; learning blocks of knowledge (memes, code, etc.) and evolving the sharing to related problem domains
– Accuracy-Interpretability tradeoff in EML
– Applications and theory of EML

Important dates are as follows:
– Paper Submission Deadline: December 19, 2014
– Paper Acceptance Notification: February 20, 2015
– Final Paper Submission Deadline: March 13, 2015
– Early Registration: March 13, 2015
– Conference Dates: May 25-28, 2015

Further information about the special session and the conference can be found:
– 2015 IEEE Congress on Evolutionary Computation
http://sites.ieee.org/cec2015/
– Special Session on New Directions in EML
https://sites.google.com/site/cec2015sseml/

Best regards,
Keiki, Tim, Yusuke, Will, and Masaya

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Yusuke NOJIMA, Dr.

Dept. of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University

Gakuen-cho 1-1, Naka-ku, Sakai, Osaka 599-8531, JAPAN
Phone: +81-72-254-9198, FAX: +81-72-254-9915
Email: nojima@cs.osakafu-u.ac.jp
http://www.cs.osakafu-u.ac.jp/ci/
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

ExSTraCS – Extended Supervised Tracking and Classifying System

Ryan Urbanowicz is pleased to announce an advanced LCS for datamining:

 

This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ExSTraCS was primarily developed to address problems in epidemiological data mining to identify complex patterns relating predictive attributes in noisy datasets to disease phenotypes of interest. ExSTraCS combines a number of recent advancements into a single algorithmic platform. It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7.

 

[http://sourceforge.net/projects/exstracs/]

Educational LCS

Ryan Urbanowicz is pleased to announce the availability of an educational LCS.

The Educational Learning Classifier System (eLCS) is a set of code demos that are intended to serve as an educational resource to learn the basics of a Michigan-Style Learning Classifier System (modeled after the XCS and UCS algorithm architectures).  This resource includes 5 separate implementation demos that sequentially add major components of the algorithm in order to highlight how these components function, and how they are implemented into the algorithm code.

[http://sourceforge.net/projects/educationallcs/]

Call For Papers Special Session: Evolutionary Machine Learning for SEAL 2014

Call For Papers
Special Session: Evolutionary Machine Learning

The 10th International Conference on Simulated Evolution And Learning (SEAL 2014)
15-18 December 2014, Dunedin, New Zealand
http://seal2014.otago.ac.nz/

================

Machine learning and evolutionary computation are two major fields of computational intelligence. They share many fundamental similarities and are frequently explored together to tackle complex, large-scale, and dynamic learning problems under various sources of uncertainties.

This special session will cover a broad range of topics related to evolutionary machine learning, including novel learning algorithms and their innovative applications. We will focus on both theoretical and practical research in this field. The aim is to show how the global search performed by evolutionary methods can complement the local search of non-evolutionary methods and how the combination of the two can improve learning effectiveness and performance within a wide range of clustering, classification, regression, prediction, and control tasks.

Topics of interest include, but not limited to:

– Learning Classifier Systems

– Genetic Programming (GP) and its application to machine learning tasks

 – Evolutionary ensembles

 – Neuroevolution and its application to machine learning tasks

 – Genetic fuzzy systems

 – Hyper-parameter tuning with evolutionary methods

 – Theoretical analysis of evolutionary learning algorithms

 – Interesting practical applications

 – Advanced computing platforms for evolutionary machine learning

 – Other Genetics-Based Machine Learning: hybrid learning systems combining evolutionary techniques with machine learning methods

  ================
Important Dates:
28 July 2014, deadline for submission of full papers (<=12 pages)
29 August 2014, Notification of acceptance
16 September 2014, Deadline for camera-ready copies of accepted papers
15-18 December 2014, Conference sessions (including tutorials and workshops)
================
Paper Submission:
You should follow the SEAL 2014 Submission Web Site
(http://seal2014.otago.ac.nz/submissions.aspx). In the Main Research Topic, please choose
“Evolutionary Machine Learning ”
Special session papers are treated the same as regular conference papers. All papers will be fully refereed by a minimum of two specialized referees. Before final acceptance, all referees comments must be considered. All accepted papers that are presented at the conference will be included in the conference proceedings, to be published in Lecture Notes in Computer Science (LNCS) by Springer. Selected papers will be invited for further revision and extension for possible publication in a special issue of a SCI journal after further review Soft Computing (Springer, Impact Factor 1.124).
================
Special Session Organizers:
Dr Aaron Chen
School of Engineering and Computer Science, Victoria University of Wellington,
PO Box 600, Wellington, New Zealand.
Email: aaron.chen@ecs.vuw.ac.nz
Homepage: http://ecs.victoria.ac.nz/Main/AaronChen

Dr Will Browne
School of Engineering and Computer Science, Victoria University of Wellington,
PO Box 600, Wellington, New Zealand.
Email: will.browne@vuw.ac.nz
Homepage: http://ecs.victoria.ac.nz/Main/WillBrowne