The aim of the task force is to promote research in the area of automated algorithm design using evolutionary computation. The scope covers the automated configuration and selection of machine learning and search techniques. Automated design ranges from design of neural network architectures to determining the control flow of evolutionary algorithms and the generation of new operators and heuristics. Algorithm configuration focuses on identifying the most suitable parameters and hyper-parameters to use in machine learning and search techniques. Algorithm selection involves selecting an algorithm to apply to the problem at hand by mapping problem features to algorithms. Focus areas of the task force include but are not limited to:

• AutoML

• Automated algorithm selection

• Algorithm portfolios

• Automated design of operators

• Automated hybridization of algorithms

• Automated parameter configuration and adaptation

• Automated hyperparameter selection

• Automated feature selection

• Automated model selection

• Automated heuristic generation

• Automated operator creation

• Hyper-heuristics

• Multilevel metaheuristics

• Reactive search

• Self-configuration

• Self-adaption

• Neuroevolution

• Theoretical aspects