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Genetic Algorithms

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Artificial Intelligence for Materials Science

Part of the book series: Springer Series in Materials Science ((SSMATERIALS,volume 312))

Abstract

Genetic algorithm, a very simple but very powerful stochastic global optimizer, has been applied to many fields in search and optimization. This capture study aims to provide overall information for a genetic algorithms user to choose the most appropriate scheme for his or her specific application problem for his or her specific application problem. In this section, we mainly address three well-known genetic algorithms, namely, artificial bee colony, particle swarm optimization, and differential evolution. The evolution mechanism, current research status, and applications of different genetic algorithm have been investigated in detail for the users to choose the most appropriate strategy.

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Authors and Affiliations

  1. School of Science, Chongqing University of Posts and Telecommunications, Chongqing, China

    Shichang Li & Dengfeng Li

Authors
  1. Shichang Li
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  2. Dengfeng Li
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Correspondence to Dengfeng Li .

Editor information

Editors and Affiliations

  1. Monash Suzhou Research Institute, Suzhou, China

    Yuan Cheng

  2. Institute of Artificial Intelligence, Beihang University, Beijing, China

    Tian Wang

  3. Institute of High Performance Computing, Singapore, Singapore

    Gang Zhang

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Li, S., Li, D. (2021). Genetic Algorithms. In: Cheng, Y., Wang, T., Zhang, G. (eds) Artificial Intelligence for Materials Science. Springer Series in Materials Science, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-68310-8_5

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