It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

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

Per Instance Algorithm Configuration (PIAC) relies on features that describe problem
instances. It builds an Empirical Performance Model (EPM) from a training set made of …

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 …

Abstract The term Procedural Content Generation (PCG) refers to the (semi-) automatic
generation of game content by algorithmic means, and its methods are becoming …

The inherent difficulty of solving a continuous, static, bound-constrained and single-objective
black-box optimization problem depends on the characteristics of the problem's fitness …

In this paper, we rely on previous work proposing a modularized version of CMA-ES, which
captures several alterations to the conventional CMA-ES developed in recent years. Each …

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the
optimal configuration of which is crucial for achieving their peak performance. However …

Benchmark sets and landscape features are used to test algorithms and to train models to
perform algorithm selection or configuration. These approaches are based on the …

Abstract Exploratory Landscape Analysis provides sample-based methods to calculate
features of black-box optimization problems in a quantitative and measurable way. Many …