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Superpolynomial lower bounds for the (1+1) EA on some easy combinatorial problems

Published:12 July 2014Publication History

ABSTRACT

The (1+1) EA is a simple evolutionary algorithm that is known to be efficient on linear functions and on some combinatorial optimization problems. In this paper, we rigorously study its behavior on two easy combinatorial problems: finding the 2-coloring of a class of bipartite graphs, and constructing satisfying assignments for a class of satisfiable 2-CNF Boolean formulas. We prove that it is inefficient on both problems in the sense that the number of iterations the algorithm needs to minimize the cost functions is superpolynomial with high probability. Our motivation is to better understand the influence of problem instance structure on the runtime character of a simple evolutionary algorithm. We are interested in what kind of structural features give rise to so-called metastable states at which, with probability 1 - o(1), the (1+1) EA becomes trapped and subsequently has difficulty leaving. Finally, we show how to modify the (1+1) EA slightly in order to obtain a polynomial-time performance guarantee on both problems.

References

  1. Bengt Apsvall, Michael F. Plass, and Robert Endre Tarjan. A linear time algorithm for testing the truth of certain quantified Boolean formulas. Information Processing Letters, 8(3):121--123, 1979.Google ScholarGoogle ScholarCross RefCross Ref
  2. Benjamin Doerr, Carola Doerr, and Franziska Ebel. Lessons from the black-box: Fast crossover-based genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 781--788. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Benjamin Doerr, Daniel Johannsen, and Carola Winzen. Multiplicative drift analysis. Algorithmica, 64(4):673--697, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. William Feller. Generalization of a probability limit theorem of Cramér. Transactions of the American Mathematical Society, 54(3):361--372, 1943.Google ScholarGoogle Scholar
  5. Oliver Giel and Ingo Wegener. Evolutionary algorithms and the maximum matching problem. In Helmut Alt and Michel Habib, editors, Proceedings of the Symposium on Theoretical Aspects of Computer Science (STACS), volume 2607 of Lecture Notes in Computer Science, pages 415--426. Springer, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Jiří Matoušek and Jan Vondrák. The probabilistic method (lecture notes), March 2008. http://kam.mff.cuni.cz/~matousek/prob-ln.ps.gz.Google ScholarGoogle Scholar
  7. Colin McDiarmid. A random recolouring method for graphs and hypergraphs. Combinatorics, Probability & Computing, 2:363--365, 1993.Google ScholarGoogle ScholarCross RefCross Ref
  8. Frank Neumann, Joachim Reichel, and Martin Skutella. Computing minimum cuts by randomized search heuristics. Algorithmica, 59(3):323--342, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Pietro S. Oliveto and Carsten Witt. Erratum: Simplified drift analysis for proving lower bounds in evolutionary computation. arXiv:1211.7184 {cs.NE}.Google ScholarGoogle Scholar
  10. Pietro S. Oliveto and Carsten Witt. Simplified drift analysis for proving lower bounds in evolutionary computation. Algorithmica, 59(3):369--386, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Christos H. Papadimitriou, Alejandro A. Schaffer, and Mihalis Yannakakis. On the complexity of local search. In Proceedings of the Annual ACM Symposium on Theory of Computing (STOC), pages 438--445. ACM, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Dirk Sudholt. Crossover is provably essential for the Ising model on trees. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 1161--1167. ACM, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. David Williams. Probability with Martingales. Cambridge Mathematical Textbooks. Cambridge University Press, 1991.Google ScholarGoogle ScholarCross RefCross Ref
  14. Carsten Witt. Tight bounds on the optimization time of a randomized search heuristic on linear functions. Combinatorics, Probability and Computing, 22(2):294--318, 2013.Google ScholarGoogle ScholarCross RefCross Ref

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      cover image ACM Conferences
      GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
      July 2014
      1478 pages
      ISBN:9781450326629
      DOI:10.1145/2576768

      Copyright © 2014 ACM

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      Publication History

      • Published: 12 July 2014

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      GECCO '14 Paper Acceptance Rate180of544submissions,33%Overall Acceptance Rate1,669of4,410submissions,38%

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