Scope and Aim:
Evolutionary Computation (EC) and Neural Computation (NC) are two representative and complementary nature-inspired computational paradigms: from the bionic point of view, EC mimics the evolutionary processes on the macro level, while NC models the working mechanisms of neural systems on the micro level. From the technical point of view, EC is a family of algorithms for complex optimization, while NC is a family of representation learning methods for complex modeling. Hence, fusions of the two computational paradigms are not only biologically plausible but also technically beneficial.
Intrinsically, the two computational paradigms can be fused in two general ways. On one hand, NC methods can be incorporated into EC frameworks to improve the search efficiency/effectiveness of the EC algorithms. On the other hand, EC algorithms can be adopted to improve the performance of NC methods. During the past decades, both EC and NC have witnessed a big boom in algorithm design and applications. However, research on exploring the synergies between EC and NC, in particular the leverage of EC for enhancing the power of NC is still in its infancy . The theme of this special session — evolutionary neural computation — aims to bring together researchers investigating methods and applications in studies the interdisciplinary fields across EC and NC. The special session will be organized on IJCNN 2023.
Topics:
Topics of interest include but are not limited to:
- Neural predictors for neural architecture search
- Supernet training for neural architecture search
- Encoding methods for neural architecture search
- Visualization methods for neural architecture search
- Evolutionary optimization for neural architecture search
- Evolutionary optimization/training of neural networks
- Evolutionary robotics
- Evolutionary reinforcement learning
- Artificial life via neuroevolution
- Novelty search via neuroevolution
- Evolutionary strategies for neuroevolution
- Genetic programming for neuroevolution
- Estimation of distribution algorithms driven by neural networks
- Surrogate-assisted evolutionary algorithms driven by neural networks
- Evolutionary neural computation for applications, e.g., image classification, image semantic segmentation, natural language processing, engineering designs, etc.
Organizers:
Dr Ran Cheng
Department of Computer Science and Engineering, Southern University of Science and Technology, China
Dr Zhichao Lu
School of Software Engineering, Sun Yat-sen University, China
Prof. Kay Chen Tan
Department of Computing, the Hong Kong Polytechnic University, Hong Kong
Prof. Yaochu Jin
Faculty of Technology, Bielefeld University, Germany