Fitness landscapes were proposed in 1932 as an abstract notion for understanding
biological evolution and were later used to explain evolutionary algorithm behaviour. The …

This study develops a framework for property prediction and multi-objective optimization of
strain-hardening cementitious composites (SHCC) based on automated machine learning …

The global structure of the hyperparameter spaces of neural networks is not well understood
and it is therefore not clear which hyperparameter search algorithm will be most effective. In …

As interest in machine learning and its applications becomes more widespread, how to
choose the best models and hyper-parameter settings becomes more important. This …

Differential evolution (DE) is the most efficient evolutionary algorithm widely used to solve
continuous or discrete numerical optimization problems. However, the performance of DE …

Although Reinforcement Learning (RL) has shown to be capable of producing impressive
results, its use is limited by the impact of its hyperparameters on performance. This often …

Hyperparameter optimization (HPO) is a key component of machine learning models for
achieving peak predictive performance. While numerous methods and algorithms for HPO …

Performing analytical tasks over graph data has become increasingly interesting due to the
ubiquity and large availability of relational information. However, unlike images or …

Exploratory landscape analysis and fitness landscape analysis in general have given
valuable insight into problem hardness understanding as well as facilitating algorithm …

Neural architecture search is a promising area of research dedicated to automating the
design of neural network models. This field is rapidly growing, with a surge of methodologies …