Advertisement

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

  • Seyedali MirjaliliEmail author
  • Jin Song Dong
  • Ali Safa Sadiq
  • Hossam Faris
Chapter
Part of the Studies in Computational Intelligence book series (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.

References

  1. 1.
    Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.CrossRefGoogle Scholar
  2. 2.
    Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.CrossRefGoogle Scholar
  3. 3.
    Genlin, J. (2004). Survey on genetic algorithm. Computer Applications and Software, 2, 69–73.Google Scholar
  4. 4.
    Cant-Paz, E. (1998). A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis, 10(2), 141–171.Google Scholar
  5. 5.
    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.Google Scholar
  6. 6.
    Goldberg, D. E. (1990). A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Systems, 4(4), 445–460.zbMATHGoogle Scholar
  7. 7.
    Miller, B. L., & Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9(3), 193–212.MathSciNetGoogle Scholar
  8. 8.
    Kumar, R. (2012). Blending roulette wheel selection & rank selection in genetic algorithms. International Journal of Machine Learning and Computing, 2(4), 365.CrossRefGoogle Scholar
  9. 9.
    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.Google Scholar
  10. 10.
    Blickle, T., & Thiele, L. (1996). A comparison of selection schemes used in evolutionary algorithms. Evolutionary Computation, 4(4), 361–394.CrossRefGoogle Scholar
  11. 11.
    Collins, R. J., & Jefferson, D. R. (1991). Selection in massively parallel genetic algorithms (pp. 249–256). University of California (Los Angeles). Computer Science Department.Google Scholar
  12. 12.
    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.MathSciNetCrossRefGoogle Scholar
  13. 13.
    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.Google Scholar
  14. 14.
    Grefenstette, J. J. (1989). How genetic algorithms work: A critical look at implicit parallelism. In Proceedings 3rd International Joint Conference on Genetic Algorithms (ICGA89).Google Scholar
  15. 15.
    Syswerda, G. (1989). Uniform crossover in genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms (pp. 2–9). Morgan Kaufmann Publishers.Google Scholar
  16. 16.
    Srinivas, M., & Patnaik, L. M. (1994). Genetic algorithms: A survey. Computer, 27(6), 17–26.CrossRefGoogle Scholar
  17. 17.
    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.Google Scholar
  18. 18.
    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.Google Scholar
  19. 19.
    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.Google Scholar
  20. 20.
    Blickle, T., Fogel, D. B., & Michalewicz, Z. (Eds.). (2000). Evolutionary computation 1: Basic algorithms and operators (Vol. 1). CRC Press.Google Scholar
  21. 21.
    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.Google Scholar
  22. 22.
    Davis, L. (1985, August). Applying adaptive algorithms to epistatic domains. In IJCAI (Vol. 85, pp. 162–164).Google Scholar
  23. 23.
    Whitley, D., Timothy, S., & Daniel, S. Schedule optimization using genetic algorithms. In L Davis, (ed.), pp. 351–357.Google Scholar
  24. 24.
    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).Google Scholar
  25. 25.
    Louis, S. J., & Rawlins, G. J. (1991, July). Designer genetic algorithms: Genetic algorithms in structure design. In ICGA (pp. 53–60).Google Scholar
  26. 26.
    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.Google Scholar
  27. 27.
    Deep, K., & Thakur, M. (2007). A new mutation operator for real coded genetic algorithms. Applied Mathematics and Computation, 193(1), 211–230.MathSciNetCrossRefGoogle Scholar
  28. 28.
    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.CrossRefGoogle Scholar
  29. 29.
    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.Google Scholar
  30. 30.
    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.Google Scholar
  31. 31.
    Tsutsui, S., & Fujimoto, Y. (1993, June). Forking genetic algorithm with blocking and shrinking modes (fGA). In ICGA (pp. 206–215).Google Scholar
  32. 32.
    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.Google Scholar
  33. 33.
    Mauldin, M. L. (1984, August). Maintaining diversity in genetic search. In AAAI (pp. 247–250).Google Scholar
  34. 34.
    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.Google Scholar
  35. 35.
    Ahn, C. W., & Ramakrishna, R. S. (2003). Elitism-based compact genetic algorithms. IEEE Transactions on Evolutionary Computation, 7(4), 367–385.CrossRefGoogle Scholar
  36. 36.
    Zitova, B., & Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing, 21(11), 977–1000.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Seyedali Mirjalili
    • 1
    Email author
  • Jin Song Dong
    • 1
    • 2
  • Ali Safa Sadiq
    • 3
  • Hossam Faris
    • 4
  1. 1.Institute for Integrated and Intelligent Systems, Griffith University, NathanBrisbaneAustralia
  2. 2.Department of Computer ScienceSchool of Computing, National University of SingaporeSingaporeSingapore
  3. 3.School of Information TechnologyMonash UniversityBandar SunwayMalaysia
  4. 4.King Abdullah II School for Information Technology, The University of JordanAmmanJordan

Personalised recommendations