Metaheuristics "In the Large"

Abstract: Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. To this end, we present the vision and progress of the Metaheuristics “In the Large” project. The conceptual underpinnings of the project are: truly extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems that will enhance reproducibility and accelerate the field’s progress. We argue that, via such principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch
Anmerkungen
ISSN: 0377-2217

Ereignis
Veröffentlichung
(wo)
Freiburg
(wer)
Universität
(wann)
2024
Urheber
Swan, Jerry
Adriansen, Steven
Brownlee, Alexander E.I
Hammond, Kevin
Johnson, Colin G.
Kheiri, Ahmed
Krawiec, Faustyna
Merelo Guervós, Juan Julián
Minku, Leandro L.
Özcan, Ender
Pappa, Gisele L.
García-Sánchez, Pablo
Sörensen, Kenneth
Voß, Stefan
Wagner, Markus
White, David R.

DOI
10.1016/j.ejor.2021.05.042
URN
urn:nbn:de:bsz:25-freidok-2449407
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
14.08.2025, 10:55 MESZ

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Beteiligte

Entstanden

  • 2024

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