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Stagnation Detection Meets Fast Mutation

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13222))

Abstract

Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from a heavy-tailed distribution (“fast mutation”, Doerr et al. (2017)) and increasing the mutation strength based on a stagnation detection mechanism (Rajabi and Witt (2020)). Whereas the latter can obtain the asymptotically best probability of finding a single desired solution in a given distance, the former is more robust and performs much better when many improving solutions in some distance exist.

In this work, we propose a mutation strategy that combines ideas of both mechanisms. We show that it can also obtain the best possible probability of finding a single distant solution. However, when several improving solutions exist, it can outperform both the stagnation-detection approach and fast mutation. The new operator is more than an interleaving of the two previous mechanisms and it outperforms any such interleaving.

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References

  1. Antipov, D., Buzdalov, M., Doerr, B.: Fast mutation in crossover-based algorithms. In: Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 1268–1276. ACM (2020)

    Google Scholar 

  2. Antipov, D., Buzdalov, M., Doerr, B.: First steps towards a runtime analysis when starting with a good solution. In: Bäck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 560–573. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58115-2_39

    Chapter  Google Scholar 

  3. Antipov, D., Buzdalov, M., Doerr, B.: Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution. In: Genetic and Evolutionary Computation Conference, GECCO 2021, pp. 1115–1123. ACM (2021)

    Google Scholar 

  4. Antipov, D., Doerr, B.: Runtime analysis of a heavy-tailed \((1+(\lambda ,\lambda ))\) genetic algorithm on jump functions. In: Bäck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 545–559. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58115-2_38

    Chapter  Google Scholar 

  5. Bambury, H., Bultel, A., Doerr, B.: Generalized jump functions. In: Genetic and Evolutionary Computation Conference, GECCO 2021, pp. 1124–1132. ACM (2021)

    Google Scholar 

  6. Corus, D., Oliveto, P.S., Yazdani, D.: Automatic adaptation of hypermutation rates for multimodal optimisation. In: Foundations of Genetic Algorithms, FOGA 2021, pp. 4:1–4:12. ACM (2021)

    Google Scholar 

  7. Corus, D., Oliveto, P.S., Yazdani, D.: Fast immune system-inspired hypermutation operators for combinatorial optimization. IEEE Trans. Evol. Comput. 25, 956–970 (2021)

    Article  Google Scholar 

  8. Dang, D., Friedrich, T., Kötzing, T., Krejca, M.S., Lehre, P.K., Oliveto, P.S., Sudholt, D., Sutton, A.M.: Escaping local optima using crossover with emergent diversity. IEEE Trans. Evol. Comput. 22, 484–497 (2018)

    Article  Google Scholar 

  9. Doerr, B.: Does comma selection help to cope with local optima? In: Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 1304–1313. ACM (2020)

    Google Scholar 

  10. Doerr, B., Doerr, C.: Theory of parameter control for discrete black-box optimization: provable performance gains through dynamic parameter choices. In: Theory of Evolutionary Computation. NCS, pp. 271–321. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29414-4_6

    Chapter  MATH  Google Scholar 

  11. Doerr, B., Le, H.P., Makhmara, R., Nguyen, T.D.: Fast genetic algorithms. In: Genetic and Evolutionary Computation Conference, GECCO 2017, pp. 777–784. ACM (2017)

    Google Scholar 

  12. Doerr, B., Rajabi, A.: Stagnation detection meets fast mutation. CoRR abs/2201.12158 (2022). https://arxiv.org/abs/2201.12158

  13. Doerr, B., Zheng, W.: Theoretical analyses of multi-objective evolutionary algorithms on multi-modal objectives. In: Conference on Artificial Intelligence, AAAI 2021, pp. 12293–12301. AAAI Press (2021)

    Google Scholar 

  14. Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. Theoret. Comput. Sci. 276, 51–81 (2002)

    Article  MathSciNet  Google Scholar 

  15. Friedrich, T., Göbel, A., Quinzan, F., Wagner, M.: Evolutionary algorithms and submodular functions: Benefits of heavy-tailed mutations. CoRR abs/1805.10902 (2018)

    Google Scholar 

  16. Friedrich, T., Göbel, A., Quinzan, F., Wagner, M.: Heavy-tailed mutation operators in single-objective combinatorial optimization. In: Auger, A., Fonseca, C.M., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds.) PPSN 2018. LNCS, vol. 11101, pp. 134–145. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99253-2_11

    Chapter  Google Scholar 

  17. Friedrich, T., Quinzan, F., Wagner, M.: Escaping large deceptive basins of attraction with heavy-tailed mutation operators. In: Genetic and Evolutionary Computation Conference, GECCO 2018, pp. 293–300. ACM (2018)

    Google Scholar 

  18. Prügel-Bennett, A.: When a genetic algorithm outperforms hill-climbing. Theoret. Comput. Sci. 320, 135–153 (2004)

    Article  MathSciNet  Google Scholar 

  19. Rajabi, A., Witt, C.: Self-adjusting evolutionary algorithms for multimodal optimization. In: Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 1314–1322. ACM (2020)

    Google Scholar 

  20. Rajabi, A., Witt, C.: Stagnation detection in highly multimodal fitness landscapes. In: Genetic and Evolutionary Computation Conference, GECCO 2021. pp. 1178–1186. ACM (2021)

    Google Scholar 

  21. Rajabi, A., Witt, C.: Stagnation detection with randomized local search. In: Zarges, C., Verel, S. (eds.) EvoCOP 2021. LNCS, vol. 12692, pp. 152–168. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72904-2_10

    Chapter  Google Scholar 

  22. Wegener, I.: Theoretical aspects of evolutionary algorithms. In: Orejas, F., Spirakis, P.G., van Leeuwen, J. (eds.) ICALP 2001. LNCS, vol. 2076, pp. 64–78. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-48224-5_6

    Chapter  Google Scholar 

  23. Witt, C.: On crossing fitness valleys with majority-vote crossover and estimation-of-distribution algorithms. In: Foundations of Genetic Algorithms, FOGA 2021, pp. 2:1–2:15. ACM (2021)

    Google Scholar 

  24. Wu, M., Qian, C., Tang, K.: Dynamic mutation based pareto optimization for subset selection. In: Huang, D.-S., Gromiha, M.M., Han, K., Hussain, A. (eds.) ICIC 2018. LNCS (LNAI), vol. 10956, pp. 25–35. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95957-3_4

    Chapter  Google Scholar 

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Acknowledgement

This work was supported by a public grant as part of the Investissements d’avenir project, reference ANR-11-LABX-0056-LMH, LabEx LMH and a research grant by the Danish Council for Independent Research (DFF-FNU 8021-00260B) as well as a travel grant from the Otto Mønsted foundation.

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Authors and Affiliations

  1. Laboratoire d’Informatique (LIX), CNRS, École Polytechnique Institute Polytechnique de Paris, Palaiseau, France

    Benjamin Doerr

  2. Technical University of Denmark, Kgs. Lyngby, Denmark

    Amirhossein Rajabi

Authors
  1. Benjamin Doerr
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  2. Amirhossein Rajabi
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Corresponding author

Correspondence to Amirhossein Rajabi .

Editor information

Editors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Leslie Pérez Cáceres

  2. Université du Littoral Côte d’Opale, Calais, France

    Sébastien Verel

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Doerr, B., Rajabi, A. (2022). Stagnation Detection Meets Fast Mutation. In: Pérez Cáceres, L., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2022. Lecture Notes in Computer Science, vol 13222. Springer, Cham. https://doi.org/10.1007/978-3-031-04148-8_13

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  • DOI: https://doi.org/10.1007/978-3-031-04148-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04147-1

  • Online ISBN: 978-3-031-04148-8

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