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Crossover speeds up building-block assembly

Published:07 July 2012Publication History

ABSTRACT

We re-investigate a fundamental question: how effective is crossover in combining building blocks? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter we prove that a simple GA with uniform crossover is twice as fast as the fastest EA using only standard bit mutation, up to small-order terms. The reason is that crossover effectively turns neutral mutations into improvements by combining the right building blocks at a later stage. Compared to mutation-based EAs, this makes multi-bit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from 1/n to (1+5)/2 Å 1/n H 1.618/n. Similar results are proved for k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building-block functions.

References

  1. M. Abramowitz and I. A. Stegun. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover, New York, ninth Dover printing, tenth GPO printing edition, 1964.Google ScholarGoogle Scholar
  2. M. Dietzfelbinger, B. Naudts, C. Van Hoyweghen, and I. Wegener. The analysis of a recombinative hill-climber on H-IFF. IEEE Transactions on Evolutionary Computation, 7(5):417--423, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. B. Doerr, E. Happ, and C. Klein. Crossover can provably be useful in evolutionary computation. Theoretical Computer Science, 425(0):17--33, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Droste, T. Jansen, and I. Wegener. On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science, 276:51--81, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Fischer and I. Wegener. The one-dimensional Ising model: Mutation versus recombination. Theoretical Computer Science, 344(2--3):208--225, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. Forrest and M. Mitchell. Relative building block fitness and the building block hypotheses. In Proc. of FOGA 2, pages 198--226. Morgan Kaufmann, 1993.Google ScholarGoogle Scholar
  7. T. Jansen, K. A. De Jong, and I. Wegener. On the choice of the offspring population size in evolutionary algorithms. Evolutionary Computation, 13:413--440, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. Jansen and I. Wegener. On the choice of the mutation probability for the (1+1) EA. In PPSN '00, volume 1917, pages 89--98. Springer, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. T. Jansen and I. Wegener. On the analysis of evolutionary algorithms--a proof that crossover really can help. Algorithmica, 34(1):47--66, 2002.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. T. Jansen and I. Wegener. Real royal road functions--where crossover provably is essential. Discrete Applied Mathematics, 149:111--125, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. T. Jansen and C. Zarges. Analysis of evolutionary algorithms: from computational complexity analysis to algorithm engineering. In Proc. of FOGA '11, pages 1--14. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. Kötzing, D. Sudholt, and M. Theile. How crossover helps in pseudo-Boolean optimization. In Proc. of GECCO '11, pages 989--996. ACM Press, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. Lassig and D. Sudholt. General scheme for analyzing running times of parallel evolutionary algorithms. In PPSN '10, pages 234--243. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Mitchell, S. Forrest, and J. H. Holland. The royal road function for genetic algorithms: fitness landscapes and GA performance. In Proc. of the 1st European Conference on Artificial Life, pages 245--254. MIT Press, 1992.Google ScholarGoogle Scholar
  15. M. Mitchell, J. H. Holland, and S. Forrest. When will a genetic algorithm outperform hill climbing? In Advances in Neural Information Processing Systems, pages 51--58. Morgan Kaufmann, 1994.Google ScholarGoogle Scholar
  16. F. Neumann, P. S. Oliveto, and C. Witt. Theoretical analysis of fitness-proportional selection: landscapes and efficiency. In Proc. of GECCO '09, pages 835--842. ACM Press, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. F. Neumann and M. Theile. How crossover speeds up evolutionary algorithms for the multi-criteria all-pairs-shortest-path problem. In Proc. of PPSN '10, pages 667--676. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. T. Storch and I. Wegener. Real royal road functions for constant population size. Theoretical Computer Science, 320:123--134, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. Sudholt. Crossover is provably essential for the Ising model on trees. In Proc. of GECCO '05, pages 1161--1167. ACM Press, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. D. Sudholt. General lower bounds for the running time of evolutionary algorithms. In PPSN '10, pages 124--133. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. D. Sudholt. A new method for lower bounds on the running time of evolutionary algorithms. ArXiv e-prints, 2011. Available from http://arxiv.org/abs/1109.1504.Google ScholarGoogle Scholar
  22. D. Sudholt and C. Thyssen. Running time analysis of ant colony optimization for shortest path problems. Journal of Discrete Algorithms, 2012. To appear. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. R. A. Watson and T. Jansen. A building-block royal road where crossover is provably essential. In Proc. of GECCO '07, pages 1452--1459. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. I. Wegener and C. Witt. On the optimization of monotone polynomials by simple randomized search heuristics. Combinatorics, Probability and Computing, 14:225--247, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. C. Witt. Runtime analysis of the (¼+1) EA on simple pseudo-Boolean functions. Evolutionary Computation, 14(1):65--86, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. C. Witt. Optimizing Linear Functions with Randomized Search Heuristics - The Robustness of Mutation. In Proc. of STACS '12, pages 420--431, 2012.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation
      July 2012
      1396 pages
      ISBN:9781450311779
      DOI:10.1145/2330163

      Copyright © 2012 ACM

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      Publication History

      • Published: 7 July 2012

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