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Exploring the Explorative Advantage of the Cooperative Coevolutionary (1+1) EA

  • Thomas Jansen
  • R. Paul Wiegand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

Using a well-known cooperative coevolutionary function optimization framework, a very simple cooperative coevolutionary (1+1) EA is defined. This algorithm is investigated in the context of expected optimization time. The focus is on the impact the cooperative coevolutionary approach has and on the possible advantage it may have over more traditional evolutionary approaches. Therefore, a systematic comparison between the expected optimization times of this coevolutionary algorithm and the ordinary (1+1) EA is presented. The main result is that separability of the objective function alone is is not sufficient to make the cooperative coevolutionary approach beneficial. By presenting a clear structured example function and analyzing the algorithms’ performance, it is shown that the cooperative coevolutionary approach comes with new explorative possibilities. This can lead to an immense speed-up of the optimization.

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References

  1. S. Droste, T. Jansen, G. Rudolph, H.-P. Schwefel, K. Tinnefeld, and I. Wegener (2003). Theory of evolutionary algorithms and genetic programming. In H.-P. Schwefel, I. Wegener, and K. Weinert (Eds.), Advances in Computational Intelligence, Berlin, Germany, 107–144. Springer.Google Scholar
  2. S. Droste, T. Jansen, and I. Wegener (2002). On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science 276, 51–81.zbMATHCrossRefMathSciNetGoogle Scholar
  3. J. Garnier, L. Kallel, and M. Schoenauer (1999). Rigorous hitting times for binary mutations. Evolutionary Computation 7(2), 173–203.CrossRefGoogle Scholar
  4. A. Iorio and X. Li (2002). Parameter control within a co-operative co-evolutionary genetic algorithm. In J. J. Merelo Guervós, P. Adamidis, H.-G. Beyer, J.-L. Fernández-Villacañas, and H.-P. Schwefel (Eds.), Proceedings of the Seventh Conference on Parallel Problem Solving From Nature (PPSN VII), Berlin, Germany, 247–256. Springer.Google Scholar
  5. R. Motwani and P. Raghavan (1995). Randomized Algorithms. Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  6. H. Mühlenbein (1992). How genetic algorithms really work Mutation and hillclimbing. In R. Männer and R. Manderick (Eds.), Proceedings of the Second Conference on Parallel Problem Solving from Nature (PPSN II), Amsterdam, The Netherlands, 15–25. North-Holland.Google Scholar
  7. M. A. Potter and K. A. De Jong (1994). A cooperative coevolutionary approach to function optimization. In Y. Davidor, H.-P. Schwefel, and R. Männer (Eds.), Proceedings of the Third Conference on Parallel Problem Solving From Nature (PPSN III), Berlin, Germany, 249–257. Springer.Google Scholar
  8. M. A. Potter and K. A. De Jong (2002). Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29.CrossRefGoogle Scholar
  9. G. Rudolph (1997). Convergence Properties of Evolutionary Algorithms. Hamburg, Germany: Dr. Kovač.Google Scholar
  10. E. van Nimwegen and J. P. Crutchfield (2001). Optimizing epochal evolutionary search: Population-size dependent theory. Machine Learning 45(1), 77–114.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Thomas Jansen
    • 1
  • R. Paul Wiegand
    • 2
  1. 1.FB 4, LS2Univ. DortmundDortmundGermany
  2. 2.Krasnow InstituteGeorge Mason UniversityFairfax

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