A literature survey and empirical study of meta-learning for classifier selection

I Khan, X Zhang, M Rehman, R Ali - IEEE Access, 2020 - ieeexplore.ieee.org
Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

Auto-sklearn 2.0: Hands-free automl via meta-learning

M Feurer, K Eggensperger, S Falkner… - Journal of Machine …, 2022 - jmlr.org
Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Machine learning for automated theorem proving: Learning to solve SAT and QSAT

SB Holden - Foundations and Trends® in Machine Learning, 2021 - nowpublishers.com
The decision problem for Boolean satisfiability, generally referred to as SAT, is the
archetypal NP-complete problem, and encodings of many problems of practical interest exist …

Automated algorithm selection: Survey and perspectives

P Kerschke, HH Hoos, F Neumann… - Evolutionary …, 2019 - ieeexplore.ieee.org
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 …

Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization

Y Tian, X Li, H Ma, X Zhang, KC Tan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-
objective optimization, where a number of variation operators have been developed to …

[HTML][HTML] Aslib: A benchmark library for algorithm selection

B Bischl, P Kerschke, L Kotthoff, M Lindauer… - Artificial Intelligence, 2016 - Elsevier
The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …

Per-run algorithm selection with warm-starting using trajectory-based features

A Kostovska, A Jankovic, D Vermetten… - … Conference on Parallel …, 2022 - Springer
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 …

[PDF][PDF] Auto-sklearn 2.0: The next generation

M Feurer, K Eggensperger, S Falkner… - arXiv preprint arXiv …, 2020 - researchgate.net
Automated Machine Learning, which supports practitioners and researchers with the tedious
task of manually designing machine learning pipelines, has recently achieved substantial …

A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks

Y Tian, S Peng, X Zhang, T Rodemann… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
As revealed by the no free lunch theorem, no single algorithm can outperform any others on
all classes of optimization problems. To tackle this issue, methods for recommending an …

Automated design of metaheuristic algorithms

T Stützle, M López-Ibáñez - Handbook of metaheuristics, 2019 - Springer
The design and development of metaheuristic algorithms can be time-consuming and
difficult for a number of reasons including the complexity of the problems being tackled, the …