I. AIM AND SCOPE
Most evolutionary algorithms and other meta-heuristic search
methods typically assume that there are explicit objective
functions available for fitness evaluations. In the real world,
however, such explicit objective functions may not exist in
many cases. For example, in many process industry
optimization problems, no explicit models exist for describing
the relationship between the final quality of the product and the
decision variables, such as control loop outputs and grinding
particle size in hematite grinding processes. Therefore, some
computationally very intensive numerical simulation, such as
computational fluid dynamic simulations or finite element
analysis or even physical experiments, are instead conducted as
the way to evaluate the fitness value. Thus, historical
experimental data becomes significantly important and can be
used for optimization. There are also cases where only factual
data can be collected.
For solving such optimization problems, evolutionary
optimization can be conducted only using a data-driven
approach. Data-driven evolutionary optimization can largely be
divided into two paradigms, one termed off-line data-driven
optimization, where no additional data can be sampled during
optimization, and the other is called on-line data-driven
optimization, where only a limited number of new data points
can be actively sampled during optimization. For both
paradigms of data-driven optimization, seamless integration of
machine learning techniques, such as model selection,
ensemble learning, active learning, semi-supervised learning
and transfer learning with evolutionary optimization are
essential, due to the fact that data acquisition is very expensive,
either computationally or costly.
This special issue aims to present the most recent advances in
data-driven optimization, in particular in the integration of
evolutionary algorithms and other meta-heuristic search
methods with machine learning techniques, neural networks
and fuzzy logic systems for surrogate modelling, data mining,
preference articulation, and decision-making.
II. TOPICS
The topics of this special issue include but are not limited to
the following topics:
• Surrogate-assisted optimization of computationally
expensive problems
• Adaptive sampling using active learning and statistical
learning techniques
• Surrogate model management in single and multiobjective
optimization
• Semi-supervised and transfer learning in data driven
optimization
• Machine learning for distributed data driven optimization
• Knowledge mining and transfer for data-driven
optimization
• Data-driven large scale and/or many-objective
optimization problems
• Preference modeling and articulation in multi- and manyobjective
optimization
• Real world applications including multidisciplinary
optimization
III. IMPORTANT DATES
• Paper submission deadline: January 31, 2018
• Notice of the first round review: April 15, 2018
• Revision due: June 15, 2018
• Final notice of acceptance/reject: July 30, 2018
IV. SUBMISSION
Manuscripts should be prepared according to the
“Information for Authors” section of the journal
(
http://cis.ieee.org/ieee-transactions-on-emerging-topics-incomputational-intelligence.html)
and submissions should be
done through the journal submission website:
https://mc.manuscriptcentral.com/tetci-ieee, by selecting the
Manuscript Type of “Computational Intelligence in DataDriven
Optimization” and clearly marking “Computational
Intelligence in Data-Driven Optimization Special Issue Paper”
as comments to the Editor-in-Chief. Submitted papers will be
reviewed by at least three different expert reviewers.
Submission of a manuscript implies that it is the authors
original unpublished work and is not being submitted for
possible publication elsewhere.
V. GUEST EDITORS
Dr. Chaoli Sun, Department of Computer Science and
Technology, Taiyuan University of Science and Technology,
Taiyuan, Shanxi 030024 China. Email:
chaoli.sun.cn@gmail.com
Dr. Handing Wang, Department of Computer Science,
University of Surrey, Guildford, GU2 7XH, UK. Email:
handing.wang@surrey.ac.uk
Prof. Wenli Du, School of Information Science &
Engineering, East China University of Science and Technology,
Shanghai, 200237, China. Email: wldu@ecust.edu.cn
Prof. Yaochu Jin, Department of Computer Science,
University of Surrey, Guildford, GU2 7XH, UK. Email:
yaochu.jin@surrey.ac.uk
IEEE CIS Student Activities Subcommittee invites you to get to know the pioneers and experts in the Computational Intelligence. This month "5 minutes with..." focuses on pioneer Prof. Jerry Mendel.
The CIS Education Multimedia Subcommittee intend to build up a reference centre of links to good quality short interactive multimedia demonstrations or short videos on CI. These can be suitable for a range of audiences, and can link to externally hosted resources. We invite you to submit recommendations for resources you have come across that would benefit the wider CIS community. please email suggestions to Annabel Latham (a.latham@mmu.ac.uk).