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Erschienen in:
Fundamentals of Evolutionary Machine Learning Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

21. Evolutionary Machine Learning for Space

verfasst von : Moritz von Looz, Alexander Hadjiivanov, Emmanuel Blazquez

Erschienen in: Handbook of Evolutionary Machine Learning

Verlag: Springer Nature Singapore

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Abstract

The Venn diagram of evolutionary computation, machine learning and space applications shows some intriguing overlaps. As evolutionary algorithms are often resource-intensive, they have not yet been applied in space. Nevertheless, it has been decisively demonstrated that evolutionary machine learning (EML) is a valuable tool for space, specifically in fields such as trajectory optimisation, optimal control and neuroevolution for robot control, where high-dimensional, discontinuous, sparse and/or non-linear problems abound. In the following chapter, we introduce common problems faced by the space research and application community, together with EML techniques used for generating robust, performant and, sometimes indeed, state-of-the-art solutions. The often complex mathematics behind some problems (especially in trajectory optimisation and optimal control) has been simplified to the minimum necessary to convey the essence of the challenge without encumbering the overview of the relevant EML algorithms. We hope that this chapter provides useful information to both the EML and the space communities in the form of algorithms, benchmarks and standing challenges.

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Vorheriges Kapitel Evolutionary Machine Learning in Medicine
Nächstes Kapitel Evolutionary Machine Learning in Control
Fußnoten
1
Other considerations include time of flight, radiation load, timing of manoeuvres and targets of opportunity.
 
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Metadaten
Titel
Evolutionary Machine Learning for Space
verfasst von
Moritz von Looz
Alexander Hadjiivanov
Emmanuel Blazquez
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Handbook of Evolutionary Machine Learning

Print ISBN: 978-981-9938-13-1

Electronic ISBN: 978-981-9938-14-8

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-3814-8_21

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