IEEE Transactions on Evolutionary Computation Special Issue on “Evolutionary Computation Meets Deep Learning”.

The goal of this Special Issue is to investigate both the new theories and methods on how deep learning can be achieved with different evolutionary computation algorithms and how evolutionary computation can be adopted in deep learning and the applications of deep learning with evolutionary computation in real-world problems. The topics of interest include, but are not limited to:

  • Evolutionary computation for learning in deep neural networks
  • Adaptive weight parameter optimization of evolutionary deep learning
  • Evolutionary neural architecture design of deep learning systems
  • Deep learning in evolutionary computation for regression/ clustering/classification
  • Evolutionary deep learning for scheduling and combinatorial optimization tasks
  • Evolutionary computation for deep learning with granular computing
  • Evolutionary multi/many-objective optimization for deep learning
  • Convergence analysis of evolutionary deep learning
  • Parallelized and distributed realizations of evolutionary deep learning
  • Co-evolution for deep learning
  • Hybridization of evolutionary fuzzy systems and memetic computing for deep learning
  • Evolutionary deep learning to real-world applications

Submission open: March 1, 2020
Submission Deadline: September 1, 2020
Guest Editors: Weiping Ding, Nantong University, China, Witold Pedrycz, University of Alberta, Canada, Gary G. Yen, Oklahoma State University, USA, Bing Xue, Victoria University of Wellington, New Zealand.

More information at

IEEE TEVC Special Issue: Evolutionary Computation Meets Deep Learning