2015/06/08 by Bernd Bischl, Pascal Kerschke, Bischl, Bernd +22 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1506.02465
Accepted to be published in Artificial Intelligence Journal
openalex publication_date 2015/06/08 · arxiv created 2016/04/06 · arxiv updated 2016/04/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
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 instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.