Skip to main content
SpringerLink
Log in
Book cover

EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation pp 289–302Cite as

Hybridizing cGAs with PSO-like Mutation

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 447))

Abstract

Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of operators andmetaheuristics have provided very powerful search techniques. In this chapter we incorporate active components of Particle Swarm Optimization (PSO) into the Cellular Genetic Algorithm(cGA).We replace themutation operator by amutation based on concepts of PSO. We present two hybrid algorithms and analyze their performance using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency, outperforming in most cases existing algorithms for a set of problems.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bäck, T. (ed.): Seventh International Conference on Genetic Algorithms. Morgan Kaufmann Publishers (1997)

    Google Scholar 

  2. Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Springer (2008)

    Google Scholar 

  3. Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation 6(5), 443–462 (2002)

    Article  Google Scholar 

  4. Cantú-Paz, E.: Eficient and Accurate Parallel Genetic Algorithms, 2nd edn. Book Series on Genetic Algorithms and Evolutionary Computation, vol. 1. Kluwer Academic (2000)

    Google Scholar 

  5. Chen, X., Li, Y.: A modified pso structure resulting in high exploration ability with convergence guaranteed. IEEE Trans. Syst., Man, Cybern. B, Cybern. 37(5), 1271–1289 (2007)

    Article  Google Scholar 

  6. Chen, Y.-P., Peng, W.-C., Jian, M.-C.: Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Trans. Syst., Man, Cybern. B, Cybern. 37(6), 1460–1470 (2007)

    Article  Google Scholar 

  7. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1(1), 3–18 (2011)

    Article  Google Scholar 

  8. Droste, S., Jansen, T., Wegener, I.: A natural and simple function which is hard for all evolutionary algorithms. In: 3rd Asia-Pacific Conf. Simulated Evol. Learning, pp. 2704–2709 (2000)

    Google Scholar 

  9. Eberhart, R., Kennedy, J.: A new optimizer using particles swarm theory. In: Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43. IEEE Service Center, Piscataway (1995)

    Chapter  Google Scholar 

  10. Goldberg, D., Deb, K., Horn, J.: Massive multimodality, deception, and genetic algorithms. In: Männer, R., Manderick, B. (eds.) Int. Conf. Parallel Prob. Solving from Nature II, pp. 37–46 (1992)

    Google Scholar 

  11. Hart, W., Krasnogor, N., Smith, J.: Recent Advances in Memetic Algorithms. Springer (2005)

    Google Scholar 

  12. Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans. Syst., Man, Cybern. B, Cybern. 35(6), 1272–1282 (2005)

    Article  Google Scholar 

  13. De Jong, K., Potter, M., Spears, W.: Using problem generators to explore the effects of epistasis. In: 7th Int. Conf. Genetic Algorithms, pp. 338–345. Morgan Kaufmann (1997)

    Google Scholar 

  14. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE Int. Conf. Neural Netw., vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  15. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108. IEEE (1997)

    Google Scholar 

  16. Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann (2001)

    Google Scholar 

  17. Khuri, S., Bäck, T., Heitkötter, J.: An evolutionary approach to combinatorial optimization problems. In: 22nd Annual ACM Computer Science Conference, pp. 66–73 (1994)

    Google Scholar 

  18. Krohling, R.A., Coelho, L.S.: Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems. IEEE Trans. Syst., Man, Cybern. B, Cybern. 36(6), 1407–1416 (2006)

    Article  Google Scholar 

  19. MacWilliams, F., Sloane, N.: The Theory of Error-Correcting Codes. North-Holland (1977)

    Google Scholar 

  20. Manderick, B., Spiessens, P.: Fine-grained parallel genetic algorithm. In: Schaffer, J.D. (ed.) Third International Conference on Genetic Algorithms (ICGA), pp. 428–433. Morgan Kaufmann (1989)

    Google Scholar 

  21. Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: Simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)

    Article  Google Scholar 

  22. Papadimitriou, C.: Computational Complexity. Adison-Wesley (1994)

    Google Scholar 

  23. Tsutsui, S., Fujimoto, Y.: Forking genetic algorithm with blocking and shrinking modes. In: Forrest, S. (ed.) 5th International Conference on Genetic Algorithms, pp. 206–213 (1993)

    Google Scholar 

  24. Whitley, D.: Cellular genetic algorithms. In: Forrest, S. (ed.) Fifth International Conference on Genetic Algorithms (ICGA), p. 658. Morgan Kaufmann (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Málaga, Málaga, Spain

    E. Alba

  2. Emerging Technologies Laboratory, Universidad Nacional de la Patagonia Austral, Río Gallegos, Argentine

    A. Villagra

Authors
  1. E. Alba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Villagra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Alba .

Editor information

Editors and Affiliations

  1. Computer Science and Communications, Research Unit, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Emilia Tantar

  2. , Computer Science and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  3. Computer Science, and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Pascal Bouvry

  4. Bordeaux Mathematical Institute, INRIA Bordeaux-Sud Ouest, Université Bordeaux I, cours de la Libération 351, Talence Cedex, 33405, France

    Pierre Del Moral

  5. Bâtiment Leyteire, UFR Sciences et Modélisation, Université Bordeaux II, 3ter place de la Victoire, Bordeaux, 33000, France

    Pierrick Legrand

  6. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Carlos A. Coello Coello

  7. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Oliver Schütze

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Berlin Heidelberg

About this chapter

Cite this chapter

Alba, E., Villagra, A. (2013). Hybridizing cGAs with PSO-like Mutation. In: Tantar, E., et al. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol 447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32726-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32726-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32725-4

  • Online ISBN: 978-3-642-32726-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation