Hybridizing Cellular GAs with Active Components of Bio-inspired Algorithms

  • E. Alba
  • A. Villagra
Part of the Studies in Computational Intelligence book series (SCI, volume 434)


Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO) are two powerful metaheuristics being used successfully since their creation for the resolution of optimization problems. In this work we present two hybrid algorithms based on a cGA with the insertion of components from PSO. We aim to achieve significant numerical improvements in the results obtained by a cGA in combinatorial optimization problems. We here analyze the performance of our hybrids using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency.


Genetic Algorithm Particle Swarm Optimization Hybrid Algorithm Parallel Genetic Algorithm Particle Swarm Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Springer (2008)Google Scholar
  2. 2.
    Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation 6(5), 443–462 (2002)CrossRefGoogle Scholar
  3. 3.
    Alba, E., Villagra, A.: Inserting active components of particle swarm optimization in cellular genetic algorithms. In: EVOLVE A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation (2011)Google Scholar
  4. 4.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press (1996)Google Scholar
  5. 5.
    Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press (1997)Google Scholar
  6. 6.
    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
  7. 7.
    Droste, S., Jansen, T., Wegener, I.: A natural and simple function which is hard for all evolutionary algorithms. In: 3rd SEAL, pp. 2704–2709 (2000)Google Scholar
  8. 8.
    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, PPSN II, pp. 37–46 (1992)Google Scholar
  9. 9.
    Hart, W., Krasnogor, N., Smith, J.: Recent Advances in Memetic Algorithms. Springer (2005)Google Scholar
  10. 10.
    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
  11. 11.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE Int. Conf. Neural Netw., vol. 4, pp. 1942–1948 (1995)Google Scholar
  12. 12.
    Kennedy, J., Eberhart, R.: A Discrete Binary Version of the Particle Swarm Algorithm. A discrete binary version of the particle swarm algorithm (1997)Google Scholar
  13. 13.
    Khuri, S., Bäck, T., Heitkötter, J.: An evolutionary approach to combinatorial optimization problems. In: 22nd Annual ACM C.S. Conf., pp. 66–73 (1994)Google Scholar
  14. 14.
    MacWilliams, F., Sloane, N.: The Theory of Error-Correcting Codes. North-Holland (1977)Google Scholar
  15. 15.
    Manderick, B., Spiessens, P.: Fine-grained parallel genetic algorithm. In: Schaffer, J.D. (ed.) 3rd ICGA, pp. 428–433. Morgan Kaufmann (1989)Google Scholar
  16. 16.
    Papadimitriou, C.: Computational Complexity. Adison-Wesley (1994)Google Scholar
  17. 17.
    Schaffer, J.D., Eshelman, L.J.: On Crossover as an Evolutionary Viable Strategy. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the 4th ICGA, pp. 61–68. Morgan Kaufmann (1991)Google Scholar
  18. 18.
    Stinson, D.: An Introduction to the Design and Analysis of Algorithms. The Charles Babbage Research Centre, St. Pierre (1985)Google Scholar
  19. 19.
    Tomassimi, M.: The parallel genetic cellular automata: Application to global function optimization. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds.) International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 385–391. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  20. 20.
    Tsutsui, S., Fujimoto, Y.: Forking genetic algorithm with blocking and shrinking modes. In: Forrest, S. (ed.) 5th ICGA, pp. 206–213 (1993)Google Scholar
  21. 21.
    Whitley, D.: Cellular genetic algorithms. In: Forrest, S. (ed.) 5th ICGA, p. 658. Morgan Kaufmann (1993)Google Scholar
  22. 22.
    Wilson, E.O.: Sociobiology: The New Systhesis. Belknap Press (1975)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MálagaMálagaSpain
  2. 2.Emerging Technologies LaboratoryUniversidad Nacional de la Patagonia AustralRío GallegosArgentine

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