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Self-tuning geometric semantic Genetic Programming

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Abstract

The process of tuning the parameters that characterize evolutionary algorithms is difficult and can be time consuming. This paper presents a self-tuning algorithm for dynamically updating the crossover and mutation probabilities during a run of genetic programming. The genetic operators that are considered in this work are the geometric semantic genetic operators introduced by Moraglio et al. Differently from other existing self-tuning algorithms, the proposed one works by assigning a (different) crossover and mutation probability to each individual of the population. The experimental results we present show the appropriateness of the proposed self-tuning algorithm: on seven different test problems, the proposed algorithm finds solutions of a quality that is better than, or comparable to, the one achieved using the best known values for the geometric semantic crossover and mutation rates for the same problems. Also, we study how the mutation and crossover probabilities change during the execution of the proposed self-tuning algorithm, pointing out an interesting insight: mutation is basically the only operator used in the exploration phase, while crossover is used for exploitation, further improving good quality solutions.

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References

  1. F. Archetti, S. Lanzeni, E. Messina, L. Vanneschi, Genetic programming for computational pharmacokinetics in drug discovery and development. Genet. Progr. Evol. Mach. 8(4), 413–432 (2007)

    Article  Google Scholar 

  2. T. Bäck, Self-adaptation in genetic algorithms, in Proceedings of the First European Conference on Artificial Life (MIT Press, 1992), pp. 263–271

  3. T. Bäck, Optimal mutation rates in genetic search, in Proceedings of the 5th International Conference on Genetic Algorithms (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993), pp. 2–8

  4. T. Brooks, D. Pope, A. Marcolini, Airfoil self-noise and prediction. Technical report, NASA RP-1218 (1989)

  5. M. Castelli, L. Manzoni, L. Vanneschi, Parameter tuning of evolutionary reactions systems, in GECCO ’12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference (ACM, Philadelphia, Pennsylvania, USA, 2012), pp. 727–734

  6. M. Castelli, L. Vanneschi, S. Silva, Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. Expert Syst. Appl. 40(17), 6856–6862 (2013)

    Article  Google Scholar 

  7. M. Castelli, L. Vanneschi, S. Silva, Prediction of the unified parkinson’s disease rating scale assessment using a genetic programming system with geometric semantic genetic operators. Expert Syst. Appl. 41(10), 4608–4616 (2014)

    Article  Google Scholar 

  8. M. Castelli, L. Vanneschi, A. Popovič, Parameter evaluation of geometric semantic genetic programming in pharmacokinetics. Int. J. Bio-Inspir. Comput. 2, 1–10 (2015)

  9. L. Davis, Adapting operator probabilities in genetic algorithms, in Proceedings of the Third International Conference on Genetic Algorithms (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1989), pp. 61–69

  10. A. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms. Evol. Comput. IEEE Trans. 3(2), 124–141 (1999)

    Article  Google Scholar 

  11. A. Eiben, Z. Michalewicz, M. Schoenauer, J. Smith, Parameter control in evolutionary algorithms, in Parameter Setting in Evolutionary Algorithms, Studies in Computational Intelligence, vol. 54, ed. by F. Lobo, C. Lima, Z. Michalewicz (Springer, Berlin, 2007), pp. 19–46

    Google Scholar 

  12. Y. I-Cheng, Simulation of concrete slump using neural networks. Constr. Mater. 162(1), 11–18 (2009)

    Google Scholar 

  13. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA, 1992)

    MATH  Google Scholar 

  14. J.R. Koza, Human-competitive results produced by genetic programming. Genet. Progr. Evol. Mach. 11(3–4), 251–284 (2010)

    Article  Google Scholar 

  15. K. Krawiec, P. Lichocki, Approximating geometric crossover in semantic space, in GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (ACM, Montreal, 2009), pp. 987–994

  16. A. Moraglio, A. Mambrini, Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression, in GECCO ’13: Genetic and Evolutionary Computation Conference (Amsterdam, The Netherlands, 2013) pp. 989–996, 6–10 July 2013

  17. A. Moraglio, K. Krawiec , C.G. Johnson, Geometric semantic genetic programming, in Parallel Problem Solving from Nature, PPSN XII (part 1). Lecture Notes in Computer Science, vol. 7491, (Springer, 2012), pp. 21–31

  18. A. Moraglio, J. Togelius, S. Silva, Geometric differential evolution for combinatorial and programs spaces. Evolut. Comput. 21(4), 591–624 (2013)

    Article  Google Scholar 

  19. I. Ortigosa , R. Lopez, J. Garcia, A neural networks approach to residuary resistance of sailing yachts prediction, in Proceedings of the International Conference on Marine Engineering MARINE, vol. 2007 (2007), p. 250

  20. Z. Ren, H. Jiang, J. Xuan, Z. Luo, Hyper-heuristics with low level parameter adaptation. Evol Comput 20(2), 189–227 (2012)

    Article  Google Scholar 

  21. J. Smith, T. Fogarty, Self adaptation of mutation rates in a steady state genetic algorithm, in Proceedings of IEEE International Conference on Evolutionary Computation, 1996, pp. 318–323

  22. C.R. Stephens, I.G. Olmedo, J.M. Vargas, H. Waelbroeck, Self-adaptation in evolving systems. Artif Life 4(2), 183–201 (1998)

    Article  Google Scholar 

  23. A. Tuson, P. Ross, Adapting operator settings in genetic algorithms. Evol. Comput. 6(2), 161–184 (1998)

    Article  Google Scholar 

  24. L. Vanneschi, M. Castelli, L. Manzoni, S. Silva, in A New Implementation of Geometric Semantic GP and its Application to Problems in Pharmacokinetics, eds. by K. Krawiec, A. Moraglio, T. Hu, A.S. Uyar, B. Hu Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013, Springer, Vienna, Austria, LNCS, vol. 7831, (2013a), pp. 205–216

  25. L. Vanneschi, S. Silva, M. Castelli, L. Manzoni, Geometric semantic genetic programming for real life applications, in Genetic Programming Theory and Practice, ed. by R. Riolo, J.H. Moore, M. Kotanchek (Springer, Ann Arbor, 2013b)

  26. L. Vanneschi, M. Castelli, S. Silva, A survey of semantic methods in genetic programming. Genet. Progr. Evol. Mach. 15(2), 195–214 (2014)

    Article  Google Scholar 

  27. D. Whitley, S. Dominic, R. Das, C.W. Anderson, Genetic reinforcement learning for neurocontrol problems. Mach. Learn. 13(2–3), 259–284 (1993)

    Article  Google Scholar 

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Acknowledgments

This work was supported by national funds through FCT under contract MassGP (PTDC/EEI-CTP/ 2975/2012), Portugal.

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Correspondence to Mauro Castelli.

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Castelli, M., Manzoni, L., Vanneschi, L. et al. Self-tuning geometric semantic Genetic Programming. Genet Program Evolvable Mach 17, 55–74 (2016). https://doi.org/10.1007/s10710-015-9251-7

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  • DOI: https://doi.org/10.1007/s10710-015-9251-7

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