On the other hand, EC is a class of population-based iterative algorithms, which generate abundant data about the search space, problem feature and population information during the optimization process. Therefore, the data mining and machine learning techniques can also be used to analyze these data for improving the performance of EC. A lot of successful applications have been reported, including the creation of new optimization paradigm such as Estimation of Distribution Algorithm, the adaptation of parameters or operators in an algorithm, mining the external archive for promising search regions, and so on.
However, there remain many open issues and opportunities that are continually emerging as intriguing challenges for bridging the gaps between EC and DM. The aim of this special session is to serve as a forum for scientists in this field to exchange the latest advantages in theories, technologies, and practice.
We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:
- EC enhanced by Data Mining and Machine Learning Concepts and/or Framework
- Data Mining and Machine Learning Based on EC techniques
- Machine Learning Enhanced and/or Model-based Multi- and/or Many-objective Optimization
- Data Mining and Machine Learning Enhanced Constrained Optimization:
- Data Mining and Machine Learning Enhanced Memetic Computation or Local Search
- Data Mining and Machine Learning Enhanced EC for Combinatorial Optimization
- Data Mining and Machine Learning Enhanced EC for Large-scale Optimization
- Data Mining and Machine Learning Enhanced EC for Dynamic Optimization
- Association Rule Mining Based on Multi-Objective Optimization
- Knowledge Discovery in Data Mining via Evolutionary Algorithm
- Genetic Programming in Data Mining
- Multi-Agent Data Mining using Evolutionary Computation
- Medical Data Mining with Evolutionary Computation
- Evolutionary Computation in Intelligent Network Management
- Evolutionary Clustering in Noisy Data Sets
- Big Data Projects with Evolutionary Computation
- Deep Learning with Evolutionary Computation
- Real World Applications
All papers should be submitted electronically through IEEE CEC 2017 website at http://www.cec2017.org/ To submit your papers to the special session, please select the Special Session in the Main Research topic. For more submission information please visit: http://www.cec2017.org/ All accepted papers will be published in the IEEE CEC 2017 electronic proceedings.
Shantou University, China
Nanjing University of Aeronautics and Astronautics, China