Special Issue on Benchmarking of Computational Intelligence Algorithms
in Computational Intelligence – An International Journal published by
Wiley Periodicals, Inc.
http://iao.hfuu.edu.cn/bocia-ci-si
Computational Intelligence (CI) is a huge and expanding field which is
rapidly gaining importance, attracting more and more interests from
both academia and industry. It includes a wide and ever-growing
variety of optimization and machine learning algorithms, which, in
turn, are applied to an even wider and faster growing range of
different problem domains. For all of these domains and application
scenarios, we want to pick the best algorithms. Actually, we want to
do more, we want to improve upon the best algorithm. This requires a
deep understanding of the problem at hand, the performance of the
algorithms we have for that problem, the features that make instances
of the problem hard for these algorithms, and the parameter settings
for which the algorithms perform the best. Such knowledge can only be
obtained empirically, by collecting data from experiments, by
analyzing this data statistically, and by mining new information from
it. Benchmarking is the engine driving research in the fields of
optimization and machine learning for decades, while its potential has
not been fully explored. Benchmarking the algorithms of Computational
Intelligence is an application of Computational Intelligence itself!
This special issue of the EI/SCI-indexed Computational Intelligence
journal published by Wiley Periodicals Inc. solicits novel
contributions from this domain according to the topics of interest
listed below.
1. Topics of Interest
The topics of interest for this workshop include, but are not limited to:
o mining of higher-level information from experimental results
o modelling of algorithm behaviors and performance
o visualizations of algorithm behaviors and performance
o statistics for performance comparison (robust statistics, PCA, ANOVA,
statistical tests, ROC, …)
o evaluation of real-world goals such as algorithm robustness,
reliability, and implementation issues
o theoretical results for algorithm performance comparison
o comparison of theoretical and empirical results
o new benchmark problems
o automatic algorithm configuration
o algorithm selection
o the comparison of algorithms in "non-traditional" scenarios such as
- multi- or many-objective domains
- parallel implementations, e.g., using GGPUs, MPI, CUDA, clusters, or
running in clouds
- large-scale problems or problems where objective function evaluations
are costly
- dynamic problems or where the objective functions involve randomized
simulations or noise
- deep learning and big data setups
o comparative surveys with new ideas on
- dos and don'ts, i.e., best and worst practices, for algorithm
performance comparison
- tools for experiment execution, result collection, and algorithm
comparison
- benchmark sets for certain problem domains and their mutual advantages
and weaknesses
2. Important Dates
Pre-Submission Deadline: 05 May 2018
Paper Submission Deadline: 05 June 2018
Expected Notification: 05 August 2018
Resubmission (Revision): 05 October 2018
Expected Notification (Revision): 05 November 2018
Final Submission: 05 December 2018
Decision Notification: 05 February 2019
Publication: 2019
3. Submission Process:
The papers of this special issue will be processed according to the
journal’s special issue guidelines at
http://projects.cs.dal.ca/ci/Special-issue-instructions.html. This
requires a preliminary submission step, for which the papers should be
sent by the pre-submission deadline to [log in to unmask] with CC to
[log in to unmask], and [log in to unmask]
4. Guest Editors
Prof. Dr. Thomas Weise obtained the MSc in Computer Science in 2005 from
the Chemnitz University of Technology and his PhD from the University of
Kassel in 2009. He then joined the University of Science and Technology of
China (USTC) as PostDoc and subsequently became Associate Professor at the
USTC-Birmingham Joint Research Institute in Intelligent Computation and Its
Applications (UBRI) at USTC. In 2016, he joined Hefei University as Full
Professor to found the Institute of Applied Optimization at the Faculty of
Computer Science and Technology. Prof. Weise has more than seven years of
experience as a full time researcher in China, having contributed
significantly both to fundamental as well as applied research. He has more
than 80 scientific publications in international peer reviewed journals and
conferences. His book "Global Optimization Algorithms – Theory and
Application" has been cited more than 730 times. He has acted as reviewer,
editor, or programme committee member at 70 different venues.
Prof. Dr. Bin Li received the B.S. degree from Hefei University of
Technology, Hefei, China, in 1992, the M.Sc. degree from Institute of
Plasma Physics, China Academy of Science, Hefei, China, in 1995, and the
Ph.D. degree from University of Science and Technology of China (USTC),
China in 2001. He is currently a professor with the School of Information
Science and Technology, USTC, Hefei, China. He has authored and co-authored
more than 40 refereed publications. His major research interests include
evolutionary computation, memetic algorithms, pattern recognition, and
real-world applications. Dr. Li is the Founding Chair of IEEE CIS Hefei
Chapter, Counselor of IEEE USTC Student Branch, senior member of Chinese
Institute of Electronics (CIE), member of the Technical Committee of
Electronic Circuits and Systems Section of CIE.
Dr. Markus Wagner is a Senior Lecturer at the School of Computer Science,
University of Adelaide, Australia. He has done his PhD studies at the Max
Planck Institute for Informatics in Saarbrücken, Germany and at the
University of Adelaide, Australia. His research topics range from
mathematical runtime analysis of heuristic optimization algorithms and
theory-guided algorithm design to applications of heuristic methods to
renewable energy production, professional team cycling and software
engineering. So far, he has been a program committee member 30 times, and
he has written over 70 articles with over 70 different co-authors. He has
chaired several education-related committees within the IEEE CIS, is Co-
Chair of ACALCI 2017 and General Chair of ACALCI 2018.
Prof. Dr. Xingyi Zhang received the B.Sc. from Fuyang Normal College in
2003, and the M.Sc. in 2006 and Ph.D. in 2009 both from Huazhong University
of Science and Technology. Currently, he is a professor in the School of
Computer Science and Technology, Anhui University. His main research
interests include unconventional models and algorithms of computation,
multi-objective evolutionary optimization and membrane computing. He is the
chair of 2017 Data Driven Optimization of Complex Systems and Applications
and 2015 Asian Conference on Membrane Computing. He also serves as
Editorial Board Member of Complex & Intelligent Systems and International
Journal of Bio-inspired Computing.
Prof. Dr. Jörg Lässig leads the Enterprise Application Development Group of
the University of Applied Sciences Zittau/Görlitz in Germany since 2011. He
holds degrees in Computer Science, Computational Physics, and received a
Ph.D. in Computer Science for his research on efficient algorithms and
models for the generation and control of cooperation networks at Chemnitz
University of Technology (Germany), which he finished in 2009. Prof. Lässig
has PostDocs at the Università della Svizzera italiana, Institute of
Computational Sciences (Lugano, Switzerland) and the International Computer
Science Institute Berkeley (California, USA). His research directions
include sustainable IT services, energy efficiency benchmarking, regional
carbon footprints and energy informatics.
5. Related Event: BOCIA
International Workshop on Benchmarking of Computational Intelligence
Algorithms (BOCIA) at the Tenth International Conference on Advanced
Computational Intelligence (ICACI 2018), March 29-31, 2018 in Xiamen,
China, http://iao.hfuu.edu.cn/bocia18
All accepted papers in this workshop will be included in the
Proceedings of the IEEE ICACI 2018 published by IEEE Press and indexed
by EI. Authors of selected papers will be invited to submit extended
versions of these papers to the Special Issue on Benchmarking of
Computational Intelligence Algorithms appearing in the Computational
Intelligence journal by Wiley Periodicals Inc., indexed by EI and SCI
(see the last page of this CfP, http://iao.hfuu.edu.cn/bocia-ci-si,
and http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8640).
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