Sparkle: Towards Accessible Meta-Algorithmics for Improving the State of the Art in Solving Challenging Problems

Koen van der Blom*, Holger Hoos, Chuan Luo, Jeroen Rook

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Many fields of computational science advance through improvements in the algorithms used for solving key problems. These advancements are often facilitated by benchmarks and competitions that enable performance comparisons and rankings of solvers. Simultaneously, meta-algorithmic techniques, such as automated algorithm selection and configuration, enable performance improvements by utilizing the complementary strengths of different algorithms or configurable algorithm components. In fact, meta-algorithms have become major drivers in advancing the state of the art in solving many prominent computational problems. However, meta-algorithmic techniques are complex and difficult to use correctly, while their incorrect use may reduce their efficiency, or in extreme cases, even lead to performance losses. Here, we introduce the Sparkle platform, which aims to make meta-algorithmic techniques more accessible to nonexpert users, and to make these techniques more broadly available in the context of competitions, to further enable the assessment and advancement of the true state of the art in solving challenging computational problems. To achieve this, Sparkle implements standard protocols for algorithm selection and configuration that support easy and correct use of these techniques. Following an experiment, Sparkle generates a report containing results, problem instances, algorithms, and other relevant information, for convenient use in scientific publications.

Original languageEnglish
Pages (from-to)1351-1364
Number of pages14
JournalIEEE Transactions on Evolutionary Computation
Volume26
Issue number6
Early online date17 Oct 2022
DOIs
Publication statusPublished - 1 Dec 2022

Keywords

  • Software tools
  • Algorithm configuration
  • Algorithm selection
  • Meta-algorithms
  • Benchmarking
  • Competitions

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