genetic algorithm theory literature review and application in image reconstruction

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

First Online: 02 February 2019

Cite this chapter

genetic algorithm theory literature review and application in image reconstruction

Seyedali Mirjalili 5 ,
Jin Song Dong 5 , 6 ,
Ali Safa Sadiq 7 &
Hossam Faris 8

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

3344 Accesses

104 Citations

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

Get 10 units per month
Download Article/Chapter or eBook
1 Unit = 1 Article or 1 Chapter
Cancel anytime
Available as EPUB and PDF
Read on any device
Instant download
Own it forever
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info
Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Nature Inspired Optimization Techniques for Image Processing—A Short Review

Certain Applications and Case Studies of Evolutionary Computing Techniques for Image Processing

Holland, J. H. (1992). Genetic algorithms. Scientific American , 267 (1), 66–73.

Article Google Scholar

Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning , 3 (2), 95–99.

Genlin, J. (2004). Survey on genetic algorithm. Computer Applications and Software , 2 , 69–73.

Google Scholar

Cant-Paz, E. (1998). A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis , 10 (2), 141–171.

Goldberg, D. E., & Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Foundations of Genetic Algorithms (Vol. 1, pp. 69–93). Elsevier.

Goldberg, D. E. (1990). A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Systems , 4 (4), 445–460.

MATH Google Scholar

Miller, B. L., & Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems , 9 (3), 193–212.

MathSciNet Google Scholar

Kumar, R. (2012). Blending roulette wheel selection & rank selection in genetic algorithms. International Journal of Machine Learning and Computing , 2 (4), 365.

Syswerda, G. (1991). A study of reproduction in generational and steady-state genetic algorithms. In Foundations of Genetic Algorithms (Vol. 1, pp. 94–101). Elsevier.

Blickle, T., & Thiele, L. (1996). A comparison of selection schemes used in evolutionary algorithms. Evolutionary Computation , 4 (4), 361–394.

Collins, R. J., & Jefferson, D. R. (1991). Selection in massively parallel genetic algorithms (pp. 249–256). University of California (Los Angeles). Computer Science Department.

Ishibuchi, H., & Yamamoto, T. (2004). Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems , 141 (1), 59–88.

Article MathSciNet Google Scholar

Hutter, M. (2002, May). Fitness uniform selection to preserve genetic diversity. In Proceedings of the 2002 Congress on Evolutionary Computation, CEC’02 . (Vol. 1, pp. 783–788). IEEE.

Grefenstette, J. J. (1989). How genetic algorithms work: A critical look at implicit parallelism. In Proceedings 3rd International Joint Conference on Genetic Algorithms (ICGA89) .

Syswerda, G. (1989). Uniform crossover in genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms (pp. 2–9). Morgan Kaufmann Publishers.

Srinivas, M., & Patnaik, L. M. (1994). Genetic algorithms: A survey. Computer , 27 (6), 17–26.

Semenkin, E., & Semenkina, M. (2012, June). Self-configuring genetic algorithm with modified uniform crossover operator. In International Conference in Swarm Intelligence (pp. 414–421). Springer, Berlin, Heidelberg.

Hu, X. B., & Di Paolo, E. (2007, September). An efficient genetic algorithm with uniform crossover for the multi-objective airport gate assignment problem. In IEEE Congress on Evolutionary Computation CEC 2007 (pp. 55–62). IEEE.

Tsutsui, S., Yamamura, M., & Higuchi, T. (1999, July). Multi-parent recombination with simplex crossover in real coded genetic algorithms. In Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1 (pp. 657–664). Morgan Kaufmann Publishers Inc.

Blickle, T., Fogel, D. B., & Michalewicz, Z. (Eds.). (2000). Evolutionary computation 1: Basic algorithms and operators (Vol. 1). CRC Press.

Oliver, I. M., Smith, D., & Holland, J. R. (1987). Study of permutation crossover operators on the travelling salesman problem. In Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms July 28–31. (1987). at the Massachusetts institute of technology (p. 1987) Cambridge, MA. Hillsdale, NJ: L. Erlhaum Associates.

Davis, L. (1985, August). Applying adaptive algorithms to epistatic domains. In IJCAI (Vol. 85, pp. 162–164).

Whitley, D., Timothy, S., & Daniel, S. Schedule optimization using genetic algorithms. In L Davis, (ed.), pp. 351–357.

Grefenstette, J., Gopal, R., Rosmaita, B., & Van Gucht, D. (1985, July). Genetic algorithms for the traveling salesman problem. In Proceedings of the First International Conference on Genetic Algorithms and Their Applications (pp. 160–168).

Louis, S. J., & Rawlins, G. J. (1991, July). Designer genetic algorithms: Genetic algorithms in structure design. In ICGA (pp. 53–60).

Eshelman, L. J., Caruana, R. A., & Schaffer, J. D. (1989, December). Biases in the crossover landscape. In Proceedings of the Third International Conference on Genetic Algorithms (pp. 10–19). Morgan Kaufmann Publishers Inc.

Deep, K., & Thakur, M. (2007). A new mutation operator for real coded genetic algorithms. Applied Mathematics and Computation , 193 (1), 211–230.

Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics , 24 (4), 656–667.

Neubauer, A. (1997, April). A theoretical analysis of the non-uniform mutation operator for the modified genetic algorithm. In IEEE International Conference on Evolutionary Computation, 1997 (pp. 93–96). IEEE.

Hinterding, R. (1995, November). Gaussian mutation and self-adaption for numeric genetic algorithms. In IEEE International Conference on Evolutionary Computation, 1995 (Vol. 1, p. 384). IEEE.

Tsutsui, S., & Fujimoto, Y. (1993, June). Forking genetic algorithm with blocking and shrinking modes (fGA). In ICGA (pp. 206–215).

Oosthuizen, G. D. (1987). Supergran: a connectionist approach to learning, integrating genetic algorithms and graph induction. In Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms: July 28–31 . at the Massachusetts Institute of Technology (p. 1987) Cambridge, MA. Hillsdale, NJ: L. Erlhaum Associates.

Mauldin, M. L. (1984, August). Maintaining diversity in genetic search. In AAAI (pp. 247–250).

Ankenbrandt, C. A. (1991). An extension to the theory of convergence and a proof of the time complexity of genetic algorithms. In Foundations of genetic algorithms (Vol. 1, pp. 53–68). Elsevier.

Ahn, C. W., & Ramakrishna, R. S. (2003). Elitism-based compact genetic algorithms. IEEE Transactions on Evolutionary Computation , 7 (4), 367–385.

Zitova, B., & Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing , 21 (11), 977–1000.

Download references

Author information

Authors and affiliations.

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

Seyedali Mirjalili & Jin Song Dong

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

Jin Song Dong

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

Ali Safa Sadiq

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

Hossam Faris

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyedali Mirjalili .

Editor information

Editors and affiliations.

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

Seyedali Mirjalili

Andrew Lewis

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

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

Download citation

DOI : https://doi.org/10.1007/978-3-030-12127-3_5

Published : 02 February 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-12126-6

Online ISBN : 978-3-030-12127-3

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Genetic algorithm: Theory, literature review, and application in image reconstruction

Research output : Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Publication series

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-12127-3_5

Fingerprint

Genetic Algorithm Keyphrases 100%
Genetic Algorithm Theory Keyphrases 100%
Image Reconstruction Keyphrases 100%
Reconstruction Biochemistry, Genetics and Molecular Biology 100%
Popular Keyphrases 50%
Evolutionary Algorithms Keyphrases 50%
Random Image Keyphrases 50%
Darwinian Theory Keyphrases 50%

T1 - Genetic algorithm

T2 - Theory, literature review, and application in image reconstruction

AU - Mirjalili, Seyedali

AU - Song Dong, Jin

AU - Sadiq, Ali Safa

AU - Faris, Hossam

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85061340811&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-12127-3_5

DO - 10.1007/978-3-030-12127-3_5

M3 - Chapter

AN - SCOPUS:85061340811

T3 - Studies in Computational Intelligence

BT - Studies in Computational Intelligence

PB - Springer Verlag