LLAMA: Leveraging Learning to Automatically Manage Algorithms

TL;DR

LLAMA is an R toolkit that simplifies the development and testing of algorithm portfolio and selection methods across various problem domains, leveraging machine learning techniques.

Contribution

It introduces a modular, extensible R package for exploring algorithm selection strategies, making it easier for researchers to apply and compare different methods.

Findings

Demonstrates LLAMA's application on SAT problems

Provides a flexible platform for algorithm portfolio exploration

Highlights current capabilities and limitations of the toolkit

Abstract

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.