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/