Per-run algorithm selection with warm-starting using trajectory-based features
Per-instance algorithm selection seeks to recommend, for a given problem instance and a
given performance criterion, one or several suitable algorithms that are expected to perform …
given performance criterion, one or several suitable algorithms that are expected to perform …
Selector: selecting a representative benchmark suite for reproducible statistical comparison
Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets
that are non-redundant and are representative of typical optimization scenarios. In this …
that are non-redundant and are representative of typical optimization scenarios. In this …
Towards feature-based performance regression using trajectory data
Black-box optimization is a very active area of research, with many new algorithms being
developed every year. This variety is needed, on the one hand, since different algorithms …
developed every year. This variety is needed, on the one hand, since different algorithms …
Transfer learning analysis of multi-class classification for landscape-aware algorithm selection
In optimization, algorithm selection, which is the selection of the most suitable algorithm for a
specific problem, is of great importance, as algorithm performance is heavily dependent on …
specific problem, is of great importance, as algorithm performance is heavily dependent on …
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-and Multi-Objective Continuous Optimization Problems
In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to
numerically characterize, in particular, single-objective continuous optimization problems …
numerically characterize, in particular, single-objective continuous optimization problems …
Explainable landscape analysis in automated algorithm performance prediction
Predicting the performance of an optimization algorithm on a new problem instance is
crucial in order to select the most appropriate algorithm for solving that problem instance …
crucial in order to select the most appropriate algorithm for solving that problem instance …
Using structural bias to analyse the behaviour of modular CMA-ES
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a commonly used
iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many …
iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many …
[HTML][HTML] Opt2Vec-a continuous optimization problem representation based on the algorithm's behavior: A case study on problem classification
Abstract Characterization of the optimization problem is a crucial task in many recent
optimization research topics (eg, explainable algorithm performance assessment, and …
optimization research topics (eg, explainable algorithm performance assessment, and …
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization
The selection of the most appropriate algorithm to solve a given problem instance, known as
algorithm selection, is driven by the potential to capitalize on the complementary …
algorithm selection, is driven by the potential to capitalize on the complementary …
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
This study examines the generalization ability of algorithm performance prediction models
across various benchmark suites. Comparing the statistical similarity between the problem …
across various benchmark suites. Comparing the statistical similarity between the problem …