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

The herein proposed Python package pflacco provides a set of numerical features to
characterize single-objective continuous and constrained optimization problems. Thereby …

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 …

Many practical decision-making problems involve dynamic scenarios, where the decision
variables, conditions and/or parameters of their optimization models change over time. Such …

Although a significant advancement of differential evolution (DE) for global optimization has
been witnessed in the past two decades, the problems of premature convergence and …

Fitness landscape analysis is a data-driven technique to study the relationship between
problem characteristics and algorithm performance by characterizing the landscape features …

Many real-world problems have expensive-to-compute fitness functions and are multi-
objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such …

AutoML tackles the problem of automatically configuring machine learning pipelines to
specific data analysis problems. These pipelines may include methods for preprocessing …

In robust optimization problems, the magnitude of perturbations is relatively small.
Consequently, solutions within certain regions are less likely to represent the robust optima …

The automation of meta-heuristic algorithm configuration holds the utmost significance in
evolutionary computation. A hierarchical reinforcement learning-aware hyper-heuristic …