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

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.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.26 No.6 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1351-1364

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BENCHMARKING
COMPETITIONS
ALGORITHM SELECTION
SOFTWARE TOOLS
ALGORITHM CONFIGURATION
META-ALGORITHMS

Info Detail Spesifik

10.1109/TEVC.2022.3215013

Pernyataan Tanggungjawab

Koen van der Blom, Holger H. Hoos, Chuan Luo, Jeroen G. Rook

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