A literature survey and empirical study of meta-learning for classifier selection
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 …
The selection of appropriate classification algorithm for a particular problem is a challenging …
Auto-sklearn 2.0: Hands-free automl via meta-learning
Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …
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 …
archetypal NP-complete problem, and encodings of many problems of practical interest exist …
Automated algorithm selection: Survey and perspectives
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 …
intensely studied, different instances are best solved using different algorithms. This is …
Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization
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 …
objective optimization, where a number of variation operators have been developed to …
[HTML][HTML] Aslib: A benchmark library for algorithm selection
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-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
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 …
[PDF][PDF] Auto-sklearn 2.0: The next generation
Automated Machine Learning, which supports practitioners and researchers with the tedious
task of manually designing machine learning pipelines, has recently achieved substantial …
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
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 …
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 …
difficult for a number of reasons including the complexity of the problems being tackled, the …