Fast, flexible, and fearless: a rust framework for the modular construction of metaheuristics
- We present MAHF, a Rust framework for the modular construction and subsequent evaluation of evolutionary algorithms, but also any other metaheuristic framework, including non-population-based and constructive approaches. We achieve high modularity and flexibility by splitting algorithms into components with a uniform interface and allowing communication through a shared blackboard. Nevertheless, MAHF is aimed at being easy to use and adapt to the specific purposes of different practitioners. To this end, this paper focuses on providing a general description of the design of MAHF before illustrating its application with a variety of different use cases, ranging from simple extension of the set of implemented components and the subsequent construction of algorithms not present within the framework to hybridization approaches, which are often difficult to realize in specialized software frameworks. By providing these comprehensive examples, we aim to encourage others to utilize MAHF forWe present MAHF, a Rust framework for the modular construction and subsequent evaluation of evolutionary algorithms, but also any other metaheuristic framework, including non-population-based and constructive approaches. We achieve high modularity and flexibility by splitting algorithms into components with a uniform interface and allowing communication through a shared blackboard. Nevertheless, MAHF is aimed at being easy to use and adapt to the specific purposes of different practitioners. To this end, this paper focuses on providing a general description of the design of MAHF before illustrating its application with a variety of different use cases, ranging from simple extension of the set of implemented components and the subsequent construction of algorithms not present within the framework to hybridization approaches, which are often difficult to realize in specialized software frameworks. By providing these comprehensive examples, we aim to encourage others to utilize MAHF for their needs, evaluate its effectiveness, and improve upon its application.…
Author: | Jonathan WurthORCiDGND, Helena StegherrORCiDGND, Michael HeiderORCiDGND, Leopold LuleyORCiD, Jörg HähnerORCiDGND |
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Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/110316 |
ISBN: | 979-8-4007-0120-7OPAC |
Parent Title (English): | GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, July 15-19, 2023 |
Publisher: | ACM |
Place of publication: | New York, NY |
Editor: | Sara Silva, Luís Paquete |
Type: | Conference Proceeding |
Language: | English |
Year of first Publication: | 2023 |
Release Date: | 2023/12/20 |
First Page: | 1900 |
Last Page: | 1909 |
DOI: | https://doi.org/10.1145/3583133.3596335 |
Institutes: | Fakultät für Angewandte Informatik |
Fakultät für Angewandte Informatik / Institut für Informatik | |
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing | |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |