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 multi-objective 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 many-objective 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-in-computational-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 Data-Driven 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