NE - Neuroevolution

Description

Neuroevolution is a machine learning approach that applies evolutionary computation (EC) to constructing artificial neural networks (NNs). Compared with other neural network training methods, Neuroevolution is highly general and allows learning without explicit targets, with arbitrary neural models and network structures. Neuroevolution has been successfully used to address challenging tasks in a wide range of areas, such as reinforcement learning, supervised learning, unsupervised learning, image analysis, computer vision, and natural language processing.

The Neuroevolution track at GECCO aims to encourage knowledge exchange between interested researchers in this area. It covers advances in the theory and applications of Neuroevolution, including all different EC methods for evolving all types of neural networks, alone and in combination with other neural learning algorithms. Authors are invited to submit their original and unpublished work to this track.

Scope

More concretely, topics of interest include but are not limited to:

• Neuroevolution algorithms involving:
  • Any EC method, e.g. genetic algorithms, evolutionary strategy, and genetic programming, particle swarm optimisation, differential evolution, meta-heuristics, Quality-Diversity, and hybrid methods.
  • Any type of neural networks, e.g. Convolutional neural network (CNN), Recurrent neural network (RNN), Long short-term memory (LSTM), Deep Belief Network (DBN), transformers, and autoencoders.
  • Evolutionary neural architecture search
  • Optimisation of network hyperparameters, activation and loss functions, learning dynamics, data augmentation, and initialisation
  • Novel candidate representations
  • Novel search mechanisms
  • Novel fitness functions
  • Surrogate assisted Neuroevolution
  • Methods for improving efficiency
  • Methods for improving regularisation
  • Multi-objective Neuroevolution
  • Neuroevolution for reinforcement learning, supervised learning, unsupervised learning
  • Neuroevolution for transfer learning, one-short learning, few-short learning, multitask learning
  • Parallelised and distributed realisations of Neuroevolution
  • Combinations of Neuroevolution and other neural learning algorithms
  • Interpretable/explainable model learning
• Applications of Neuroevolution:
  • Computer vision, image processing and pattern recognition
  • Text mining, natural language processing
  • Speech recognition
  • Neural Architecture Search
  • Machine translation
  • Medical and biological problems
  • Evolutionary robotics
  • Artificial life
  • Time series analysis
  • Cyber security
  • Scheduling and combinatorial optimization
  • Healthcare
  • Finance, fraud detection and business
  • Social media data analysis
  • Game playing
  • Visualisation