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Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 811))

Abstract

Genetic Algorithm (GA) is one of the most well-regarded evolutionary algorithms in the history. This algorithm mimics Darwinian theory of survival of the fittest in nature. This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm. The most popular improvements in the main component of this algorithm (selection, crossover, and mutation) are given too. The chapter also investigates the application of this technique in the field of image processing. In fact, the GA algorithm is employed to reconstruct a binary image from a completely random image.

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Author information

Authors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili & Jin Song Dong

  2. Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore

    Jin Song Dong

  3. School of Information Technology, Monash University, 47500, Bandar Sunway, Malaysia

    Ali Safa Sadiq

  4. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris

Authors
  1. Seyedali Mirjalili
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  2. Jin Song Dong
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  3. Ali Safa Sadiq
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  4. Hossam Faris
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Corresponding author

Correspondence to Seyedali Mirjalili .

Editor information

Editors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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Mirjalili, S., Song Dong, J., Sadiq, A.S., Faris, H. (2020). Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_5

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