Topic: Evolutionary Computation for Deep Learning and Machine Learning

Evolutionary computation technique has been widely used for addressing various challenging problems due to its powerful global search ability. There are many complex optimization tasks in the fields of deep learning and machine learning, such as neural architecture search, hyper-parameter search, feature selection, feature construction, etc. This workshop aims to collect original papers that develop new evolutionary computation techniques to address deep learning and machine learning tasks in multiple aspects. For all the aforementioned, we kindly invite the scientific community to contribute to this workshop by submitting novel and original research related but not limited to the following topics:

● Neural Architecture Search (NAS)
● Hyper Parameters Optimization
● Evolutionary Deep Learning/Evolving Deep Learning
● Evolutionary Deep Neural Networks
● Evolutionary Computation for Deep Neural Networks
● Evolutionary Neural Architecture Search (ENAS)
● Evolving Generative Adversarial Networks
● Evolutionary Recurrent Neural Network
● Evolutionary Differentiable Neural Architecture Search
● Searching for Activation Functions
● Deep Neuroevolution
● Neural Networks with Evolving Structure
● AutoML
● Multi-objective Neural Architecture Search
● Evolutionary Optimization of Deep Learning
● Evolutionary Computation for Neural Architecture Search
● Hyper-parameter Tuning with Evolutionary Computation
● Hyper-parameter Optimization
● Evolutionary Computation for Hyper-parameter Optimization
● Evolutionary Computation for Automatic Machine Learning
● Evolutionary Computation for Deep Neural Network
● Evolutionary Transfer Learning
● Differentiable NAS
● Differentiable Architecture Search
● Hybridization of Evolutionary Computation and Neural Networks
● Large-scale Optimization for Evolutionary Deep Learning
● Evolutionary Multi-task Optimization in Deep Learning
● Neuroevolution
● EvolNAS
● NASNet
● Neuroevolution
● Self-adaptive Evolutionary NAS
● Hyper-parameter Tuning with Self-adaptive Evolutionary Algorithm
● Evolutionary Computation in Deep Learning for Regression/Clustering/Classification
● Full-space Neural Architecture Search
● Evolving Neural Networks
● Automatic Design of Neural Architectures
● Evolutionary Neural Networks
● Feature Selection, Extraction, and Dimensionality Reduction on High-dimensional and Large-scale Data
● Evolutionary Feature Selection and Construction
● Multi-objective Feature Selection/Multi-object classification/ Multi-object clustering
● Multi-task optimization, Multi-task learning, Meta-learning
● Learning-Based Optimization
● Hybridization of Evolutionary Computation and Cost-sensitive Classification/Clustering
● Bi-level Optimization (BLO)
● Hybridization of Evolutionary Computation and Class-imbalance Classification/Clustering
● Numerical Optimization/Combination optimization/ Multi-objective optimization
● Genetic Algorithm/Genetic Programming/Particle Swarm Optimization/Ant Colony Optimization/Artificial Bee Colony/Differential Evolution/Fireworks Algorithm/Brain Storm Optimization
● Classification/Clustering/Regression
● Machine Learning/Data Mining/Neural Network/Deep Learning/Support Vector Machine/Decision Tree/Deep Neural Network/Convolutional Neural Network/Reinforcement Learning/Ensemble Learning/K-means
● Real-world Applications of Evolutionary Computation and Machine Learning, e.g., Images and Video Sequences/Analysis, Face Recognition, Gene Analysis, Biomarker Detection, Medical Data Analysis, Text Mining, Intrusion Detection Systems, Vehicle Routing, Computer Vision, Natural Language Processing, Speech Recognition, etc.

31 Dec 2022