LLAMA: Leveraging Learning to Automatically Manage Algorithms
This provides a practical tool for researchers in algorithm selection to more easily experiment with different techniques, though it is incremental as it packages existing methods.
The authors tackled the problem of algorithm portfolio and selection systems being too customized and domain-specific, making it hard for researchers to explore techniques for their own problems, by presenting LLAMA, a modular toolkit implemented as an R package that facilitates exploration of portfolio techniques on any problem domain, as illustrated on SAT problems.
Algorithm portfolio and selection approaches have achieved remarkable improvements over single solvers. However, the implementation of such systems is often highly customised and specific to the problem domain. This makes it difficult for researchers to explore different techniques for their specific problems. We present LLAMA, a modular and extensible toolkit implemented as an R package that facilitates the exploration of a range of different portfolio techniques on any problem domain. It implements the algorithm selection approaches most commonly used in the literature and leverages the extensive library of machine learning algorithms and techniques in R. We describe the current capabilities and limitations of the toolkit and illustrate its usage on a set of example SAT problems.