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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.
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.
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.
Miller, B. L., & Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9(3), 193–212.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite 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:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12126-6
Online ISBN: 978-3-030-12127-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)