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Inclusive Genetic Programming

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Book cover Genetic Programming (EuroGP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12691))

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Abstract

The promotion and maintenance of the population diversity in a Genetic Programming (GP) algorithm was proved to be an important part of the evolutionary process. Such diversity maintenance improves the exploration capabilities of the GP algorithm, which as a consequence improves the quality of the found solutions by avoiding local optima. This paper aims to further investigate and prove the efficacy of a GP heuristic proposed in a previous work: the Inclusive Genetic Programming (IGP). Such heuristic can be classified as a niching technique, which performs the evolutionary operations like crossover, mutation and selection by considering the individuals belonging to different niches in order to maintain and exploit a certain degree of diversity in the population, instead of evolving the niches separately to find different local optima. A comparison between a standard formulation of GP and the IGP is carried out on nine different benchmarks coming from synthetic and real world data. The obtained results highlight how the greater diversity in the population, measured in terms of entropy, leads to better results on both training and test data, showing that an improvement on the generalization capabilities is also achieved.

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References

  1. Alfaro-Cid, E., Merelo, J.J., Fernández de Vega, F., Esparcia-Alcázar, A.I., Sharman, K.: Bloat control operators and diversity in genetic programming: a comparative study. Evol. Comput. 18(2), 305–332 (2010). https://doi.org/10.1162/evco.2010.18.2.18206

  2. Aslam, M.W., Zhu, Z., Nandi, A.K.: Diverse partner selection with brood recombination in genetic programming. Appl. Soft Comput. J. 67, 558–566 (2018). https://doi.org/10.1016/j.asoc.2018.03.035

    Article  Google Scholar 

  3. Beyer, H.G., Schwefel, H.P.: Evolution strategies - a comprehensive introduction. Natl. Comput. 1(1), 3–52 (2002). https://doi.org/10.1023/A:1015059928466

    Article  MathSciNet  MATH  Google Scholar 

  4. Burke, E., Gustafson, S., Kendall, G., Krasnogor, N.: Advanced population diversity measures in genetic programming. In: Guervós, J.J.M., Adamidis, P., Beyer, H.G., Schwefel, H.P., Fernández-Villacañas, J.L. (eds.) Parallel Problem Solving from Nature – PPSN VII, pp. 341–350. Springer, Berlin Heidelberg, Berlin, Heidelberg (2002)

    Google Scholar 

  5. Crepinsek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. 45(3), 1–33 (2013). https://doi.org/10.1145/2480741.2480752

    Article  MATH  Google Scholar 

  6. Day, P., Nandi, A.K.: Binary string fitness characterization and comparative partner selection in genetic programming. IEEE Trans. Evol. Comput. 12(6), 724–735 (2008). https://doi.org/10.1109/TEVC.2008.917201

    Article  Google Scholar 

  7. De Jong, E.D., Watson, R.A., Pollack, J.B.: Reducing bloat and promoting diversity using multi-objective methods. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO2001, vol. GECCO-2001, pp. 11–18 (2001)

    Google Scholar 

  8. Fernandez De Vega, F., et al.: Time and individual duration in genetic programming. IEEE Access 8, 38692–38713 (2020). https://doi.org/10.1109/ACCESS.2020.2975753

    Article  Google Scholar 

  9. Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagńe, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)

    MathSciNet  Google Scholar 

  10. Grosman, B., Lewin, D.R.: Lyapunov-based stability analysis automated by genetic programming. In: 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, pp. 766–771 (2009). https://doi.org/10.1109/CACSD-CCA-ISIC.2006.4776742

  11. Khayyam, H., Jamali, A., Assimi, H., Jazar, R.N.: Genetic programming approaches in design and optimization of mechanical engineering applications. In: Jazar, R.N., Dai, L. (eds.) Nonlinear Approaches in Engineering Applications, pp. 367–402. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-18963-1_9

    Chapter  Google Scholar 

  12. Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994). https://doi.org/10.1007/BF00175355. https://link.springer.com/content/pdf/10.1007%2FBF00175355.pdf

  13. Luke, S., Panait, L.: Fighting bloat with nonparametric parsimony pressure. In: Guervós, J.J.M., Adamidis, P., Beyer, H.G., Schwefel, H.P., Fernández-Villacañas, J.L. (eds.) Parallel Problem Solving from Nature – PPSN VII, pp. 411–421. Springer, Berlin Heidelberg, Berlin, Heidelberg (2002)

    Google Scholar 

  14. Mahfoud, S.W.: Niching methods for genetic algorithms. Ph.D. thesis (1995)

    Google Scholar 

  15. Marchetti, F., Minisci, E.: A hybrid neural network-genetic programming intelligent control approach. In: Filipič, B., Minisci, E., Vasile, M. (eds.) BIOMA 2020. LNCS, vol. 12438, pp. 240–254. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63710-1_19

    Chapter  Google Scholar 

  16. Oh, C.K., Barlow, G.J.: Autonomous controller design for unmanned aerial vehicles using multi-objective genetic programming. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), vol. 2, pp. 1538–1545 (2004). https://doi.org/10.1109/CEC.2004.1331079

  17. Rosca, J.P.: Entropy-driven adaptive representation. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pp. 23–32 (1995)

    Google Scholar 

  18. Schmidt, M., Lipson, H.: Symbolic regression of implicit equations. In: Riolo, R., O’Reilly, U.M., McConaghy, T. (eds.) Genetic Programming Theory and Practice VII, pp. 73–85. Springer, US, Boston, MA (2010). https://doi.org/10.1007/978-1-4419-1626-6_5

  19. Shir, O.M.: Niching in evolutionary algorithms. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds.) Handbook of Natural Computing, pp. 1035–1069. Springer, Berlin Heidelberg (2012). https://doi.org/10.1007/978-3-540-92910-9_32

  20. Squillero, G., Tonda, A.: Divergence of character and premature convergence: a survey of methodologies for promoting diversity in evolutionary optimization. Inf. Sci. 329, 782–799 (2016). https://doi.org/10.1016/j.ins.2015.09.056

    Article  Google Scholar 

  21. Verdier, C.F., Mazo Jr., M.: Formal controller synthesis via genetic programming. IFAC-PapersOnLine 50(1), 7205–7210 (2017). https://doi.org/10.1016/j.ifacol.2017.08.1362

    Article  Google Scholar 

  22. Žegklitz, J., Pošík, P.: Benchmarking state-of-the-art symbolic regression algorithms. Genet. Programm. Evolvable Mach. (2020). https://doi.org/10.1007/s10710-020-09387-0

    Article  Google Scholar 

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Authors and Affiliations

  1. Intelligent Computational Engineering Laboratory, University of Strathclyde, Glasgow, UK

    Francesco Marchetti & Edmondo Minisci

Authors
  1. Francesco Marchetti
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  2. Edmondo Minisci
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Correspondence to Francesco Marchetti .

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Editors and Affiliations

  1. Queen’s University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

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Marchetti, F., Minisci, E. (2021). Inclusive Genetic Programming. In: Hu, T., Lourenço, N., Medvet, E. (eds) Genetic Programming. EuroGP 2021. Lecture Notes in Computer Science(), vol 12691. Springer, Cham. https://doi.org/10.1007/978-3-030-72812-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-72812-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72811-3

  • Online ISBN: 978-3-030-72812-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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