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Artificial Intelligence in Astrophysics

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Intelligent Astrophysics

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 39))

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Abstract

Artificial intelligence and its subparts (like evolutionary algorithms, machine learning\(,\ldots \)) are search methods that can be used for solving optimization problems. A particular class of algorithms like bioinspired one mimic working principles from natural evolution (or swarm intelligence) by employing a population-based (swarm) approach, labelling each individual of the population with a fitness and including elements of random, albeit the random is directed through a selection process. In the contemporary astrophysical literature is a lot of research papers, that work with various machine learning methods, while evolutionary/swarm algorithms are almost neglected. In this chapter, we review the basic principles of evolutionary algorithms and discuss their purpose, structure and behaviour. In doing so, it is particularly shown how the fundamental understanding of natural evolutionary processes has cleared the ground for the origin of evolutionary algorithms. Major implementation variants and they are structural as well as functional elements are discussed. We also give a brief overview of usability areas of the algorithm and end with some general remarks of the limits of computing, including demonstration of its use on astrophysical data processing at the end.

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Acknowledgements

The following grants are acknowledged for the financial support provided for this research: Grant of SGS No. SP2020/78, VSB-Technical University of Ostrava.

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Authors and Affiliations

  1. Department of Computing Science, Faculty of Electrical Engineering and Computing Science, Technical University of Ostrava, Tr. 17. Listopadu 15, Ostrava, Czech Republic

    Ivan Zelinka, Thanh Cong Truong, Diep Quoc Bao, Lumir Kojecky & Eslam Amer

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  1. Ivan Zelinka
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  2. Thanh Cong Truong
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  3. Diep Quoc Bao
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  4. Lumir Kojecky
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  5. Eslam Amer
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Correspondence to Ivan Zelinka .

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Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, VÅ B-TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  2. INAF, Astronomical Observatory of Capodimonte, NAPOLI, Italy

    Massimo Brescia

  3. School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel

    Dalya Baron

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Zelinka, I., Cong Truong, T., Quoc Bao, D., Kojecky, L., Amer, E. (2021). Artificial Intelligence in Astrophysics. In: Zelinka, I., Brescia, M., Baron, D. (eds) Intelligent Astrophysics. Emergence, Complexity and Computation, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-030-65867-0_1

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