Skip to main content
SpringerLink
Log in

Genetic Algorithm

  • Chapter
  • First Online:
Evolutionary Algorithms and Neural Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 780))

Abstract

Genetic Algorithm (GA) is one of the first population-based stochastic algorithm proposed in the history. Similar to other EAs, the main operators of GA are selection, crossover, and mutation. This chapter briefly presents this algorithm and applies it to several case studies to observe its performance.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  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. 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 

  7. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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. 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 

  13. Hutter, M. (2002). 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. Grefenstette, J. J. (1989). How genetic algorithms work: A critical look at implicit parallelism. In Proceedings of the 3rd International Joint Conference on Genetic Algorithms (ICGA89).

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  19. Tsutsui, S., Yamamura, M., & Higuchi, T. (1999). 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. Bck, T., Fogel, D. B., & Michalewicz, Z. (Eds.). (2000). Evolutionary computation 1: Basic algorithms and operators (Vol. 1). CRC press.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  23. Whitley, D., Timothy, S., & Daniel, S. Schedule optimization using genetic algorithms. In D. Lawrence (Ed.) 351–357.

    Google Scholar 

  24. Grefenstette, J., Gopal, R., Rosmaita, B., & Van Gucht, D. (1985). 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. Louis, S. J., & Rawlins, G. J. (1991). Designer genetic algorithms: Genetic algorithms in structure design. In ICGA (pp. 53–60).

    Google Scholar 

  26. Eshelman, L. J., Caruana, R. A., & Schaffer, J. D. (1989). 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. Deep, K., & Thakur, M. (2007). A new mutation operator for real coded genetic algorithms. Applied Mathematics and Computation, 193(1), 211–230.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  36. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Seyedali Mirjalili

Authors
  1. Seyedali Mirjalili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyedali Mirjalili .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mirjalili, S. (2019). Genetic Algorithm. In: Evolutionary Algorithms and Neural Networks. Studies in Computational Intelligence, vol 780. Springer, Cham. https://doi.org/10.1007/978-3-319-93025-1_4

Download citation

Publish with us

Policies and ethics

Search

Navigation