Provisionally Accepted IEEE CEC 2019 Workshops

CEC-W01 Workshop on Data-driven Optimization and Applications (DDOA 2019)

Organized by Chaoli Sun, Handing Wang, Tinkle Chugh and Yaochu Jin

Supported by IEEE CIS Evolutionary Computation Technical Committee Task Force on Data-Driven Evolutionary Optimization of Expensive Problems

Scope and Topics

Not all objective functions can be formulated using explicit equations, instead, they are normally evaluated using high precise simulation or computationally expensive experiments. So although meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, have been paid more and more attention in real-world applications, they are limited for optimizing those problems with time-expensive performance evaluation on the design. Recently, the historical data are proposed to be utilized to train surrogate models using machine learning techniques in order to replace the compute-expense/time-expensive objective function during phases of the heuristic search. The successful applications can be found in aerodynamic design, structural design, drug design, and so on.

Despite surrogate-assisted meta-heuristic algorithms get successful application, there still remain many challenges for researchers to explore. For example, due to the curse of dimensionality and the insufficiency of the samples for model training, it is very difficult, if not impossible to train accurate surrogate models. Thus, appropriate model management techniques based on the characteristics of meta-heuristic algorithms play significant important role in the surrogate-assisted optimization. In addition, modern data analytics involving advance sampling techniques and learning techniques such as semi-supervised learning, transfer learning and active learning are highly beneficial for speeding up evolutionary search while bringing new insights into the problems of interest. Finally, the application problems for verifying the efficiency and effectiveness of different approaches are also indispensability.

This workshop aims to promote the research on data-driven optimization and extend meta-heuristic algorithms to solve time-expensive problems. The topics of this workshop include but are not limited to the following topics:

  • Surrogate-assisted meta-heuristic algorithms for computationally expensive problems
  • Surrogate model management in single, multi/many-objective and constrained optimization
  • Data collection approaches in surrogate-assisted optimization
  • Adaptive model selection strategies and sampling for model training using active learning and statistical techniques
  • Bayesian evolutionary optimization
  • Approximation strategies
  • Machine learning, such as deep learning for big data driven optimization
  • Data-driven optimization using big data and data analytics
  • Computationally efficient meta-heuristic algorithms for large scale and/or many-objective optimization problems
  • Real world applications including multidisciplinary optimization

Submission instructions

The following information should be sent to the workshop chair, Dr. Chaoli Sun ( or by email.

  • Theme of Workshop: Data-driven Optimization and Applications
  • Name of presenter
  • Title
  • Abstract paper and full paper
The submission format for the paper is the same as of CEC2019, at In order to participate this workshop, full or student registration of CEC 2019 is needed.

Submission deadline

Paper submission: 15 March 2019
Decision notification: 31 March 2019
Final submission: 15 April 2019

CEC-W02 Workshop on Understanding of Evolutionary Optimization Behavior

Organized by Tome Eftimov, Peter Korošec, Christian Blum

Scope and Topics

Understanding of optimization algorithm’s behavior is a vital part that is needed for quality progress in the field of stochastic optimization algorithms. Too often (new) algorithms are setup and tuned only focusing on achieving the desired optimization goal. While this might be effective and efficient in short term, in long term this is insufficient due to the fact that this needs to be repeated for every new problem that arises. Such approach provides only minor immediate gains, instead of contributing to the progress in research on optimization algorithms. To be able to overcome this deficiency, we need to establish new standards for understanding optimization algorithm behavior, which will provide understanding of the working principles behind the stochastic optimization algorithms. This includes theoretical and empirical research, which would lead to providing insight into answering questions such as (1) why does an algorithm work for some problems but does not work for others, (2) how to explore the fitness landscape to gain better understanding of the algorithm’s behavior, and (3) how to interconnect stochastic optimization and machine learning to improve the algorithm’s behavior on new unseen instances.

The focus of this workshop is to highlight theoretical and empirical research that investigate approaches needed to analyze stochastic optimization algorithms and performance assessment with regard to different criteria. The main goal is to bring the problem and importance of understanding optimization algorithms closer to researchers and to show them how and why this is important for future development in the optimization community. This will help researchers/users to transfer the gained knowledge from theory into the real world, or to find the algorithm that is best suited to the characteristics of a given real-world problem.

Topics of interest:

  • Data-driven approaches (machine learning/information theory/statistics) for assessing algorithm performance
  • Vector embeddings of problem search space
  • Meta-learning
  • New advances in analysis and comparison of algorithms
  • Operators influence on algorithm behavior
  • Parameters influence on algorithm behavior
  • Theoretical algorithm analysis

Submission instructions

All submissions should reflect the CEC2019 submission format provided at, will be handled through Easychair ( and reviewed by the program committee.

In order to participate to this workshop, full or student registration of CEC 2019 is needed.

Selected papers will be invited to be extended for a special issue in the Natural Computing.

Submission deadline

Paper submission: 15 March 2019
Decision notification: 31 March 2019
Final submission: 15 April 2019

CEC-W03 Workshop on Evolutionary classification and clustering, concept drift, novelty detection in big/fast data context

Organized by Jean-Charles Lamirel, Pascal Cuxac, Mustapha Lebbah

Scope and Topics

The development of dynamic information analysis methods, like incremental classification/clustering, concept drift management and novelty detection techniques, is becoming a central concern in a bunch of applications whose main goal is to deal with information which is varying over time or with information flows that can oversize memory storage or computation capacity. These applications relate themselves to very various and highly strategic domains, including web mining, social network analysis, adaptive information retrieval, anomaly or intrusion detection, process control and management recommender systems, technological and scientific survey, and even genomic information analysis, in bioinformatics.

The term “incremental” is often associated to the terms evolutionary, adaptive, interactive, on-line, or batch. The majority of the learning methods were initially defined in a non-incremental way. However, in each of these families, were initiated incremental methods making it possible to take into account the temporal component of a data flow or to achieve learning on huge/fast datasets in a tractable way. In a more general way incremental classification/clustering algorithms and novelty detection approaches are subjected to the following constraints:

  • Potential changes in the data description space must be taken into consideration;
  • Possibility to be applied without knowing as a preliminary all the data to be analyzed;
  • Taking into account of a new data must be carried out without making intensive use of the already considered data;
  • Result must but available after insertion of all new data.
The above mentioned constraints clearly follow the VVV (Volume-Velocity and Variety) rule and thus directly fit with big/fast data context.

This workshop aims to offer a meeting opportunity for academics and industry-related researchers, belonging to the various communities of Computational Intelligence, Machine Learning, Experimental Design, Data Mining and Big/Fast Data Management to discuss new areas of incremental classification, concept drift management and novelty detection and on their application to analysis of time varying information and huge dataset of various natures. Another important aim of the workshop is to bridge the gap between data acquisition or experimentation and model building.

Through an exhaustive coverage of the incremental learning area workshop will provide fruitful exchanges between plenaries, contributors and workshop attendees. The emerging big/fast data context will be taken into consideration in the workshop.

Topic of Interest:
  • Novelty detection algorithms and techniques
  • Semi-supervised and active learning approaches
  • Adaptive hierarchical, k-means or density based methods
  • Adaptive neural methods and associated Hebbian learning techniques
  • Multiview diachronic approaches
  • Probabilistic approaches
  • Distributed appraoches
  • Graph partitioning methods and incremental clustering approaches based on attributed graphs
  • Incremental clustering approaches based on swarm intelligence and genetic algorithms
  • Evolving classifier ensemble techniques
  • Incremental classification methods and incremental classifier evaluation
  • Dynamic variable selection techniques
  • Clustering of time series
  • Visualization methods for evolving data analysis results
  • Evolving textual information analysis
  • Evolving social network analysis
  • Dynamic process control and tracking
  • Intrusion and anomaly detection
  • Genomics and DNA micro-array data analysis
  • Adaptive recommender and filtering systems
  • Scientometrics, webometrics and technological survey

Submission instructions

The following information should be sent to the workshop chair, Dr. Handing Wang,, by email.

– Theme of Workshop: Evolutionary classification and clustering, concept drift, novelty detection
– Name of presenter : Jean-Charles Lamirel
– Title : EvoLearn-BF
– Abstract paper and full paper.

The submission format for the paper is the same as of CEC2019, at

In order to participate this workshop, full or student registration of CEC 2019 is needed.

Submission deadline

Paper submission: 1 April 2019
Decision notification: 15 April 2019
Final submission: 1 May 2019