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Hybridizing Lévy Flights and Cartesian Genetic Programming for Learning Swarm-Based Optimization

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Advances in Computational Intelligence Systems (UKCI 2023)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1453))

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Abstract

Cartesian Genetic Programming is a well-established version of Genetic Programming and has meanwhile been applied to many use cases. The case of learning swarm behavior for optimization recently showed some fitness landscape characteristics that make program evolution harder due to the intrinsic barrier structure that is hard to pass by using standard mutation. In this paper, we explore possible improvements by replacing the standard uniform mutation by Lévy flights when training with a \((\mu +\lambda )\)-evolution strategy. We demonstrate the superiority of the new variation operation for training instances of the optimization learning problem and compare success rates and minimal computational effort.

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References

  1. Bremer, J.: Learning to Optimize, pp. 1–19. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-06839-3_1

  2. Bremer, J., Lehnhoff, S.: Towards Evolutionary Emergence. Ann. Comput. Sci. Inform. Syst. 26, 55–60 (2021)

    Article  Google Scholar 

  3. Christensen, S., Oppacher, F.: An analysis of Koza’s computational effort statistic for genetic programming. In: Genetic Programming: 5th European Conference, EuroGP 2002 Kinsale, Ireland, April 3–5, 2002 Proceedings 5. pp. 182–191 (2002)

    Google Scholar 

  4. Clegg, J., Walker, J.A., Miller, J.F.: A new crossover technique for cartesian genetic programming. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1580–1587 (2007)

    Google Scholar 

  5. Diveev, A.: Cartesian genetic programming for synthesis of optimal control system. In: Proceedings of the Future Technologies Conference, pp. 205–222. Springer (2020)

    Google Scholar 

  6. Fogel, D.B., Atmar, J.W.: Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems. Biol. Cybern. 63(2), 111–114 (1990)

    Article  Google Scholar 

  7. Goldman, B.W., Punch, W.F.: Reducing wasted evaluations in cartesian genetic programming. In: European Conference on Genetic Programming, pp. 61–72. Springer (2013)

    Google Scholar 

  8. Gupta, R., Pal, R.: Biogeography-based optimization with Lévy-flight exploration for combinatorial optimization. In: 2018 8th International Conference on Cloud Computing, Data Science Engineering (Confluence), pp. 664–669 (2018)

    Google Scholar 

  9. Haklı, H., Uğuz, H.: A novel particle swarm optimization algorithm with Lévy flight. Appl. Soft Comput. 23, 333–345 (2014)

    Article  Google Scholar 

  10. Harding, S., Banzhaf, W., Miller, J.F.: A survey of self modifying cartesian genetic programming. In: Genetic Programming Theory and Practice VIII, pp. 91–107. Springer (2011)

    Google Scholar 

  11. Harding, S., Leitner, J., Schmidhuber, J.: Cartesian genetic programming for image processing. In: Genetic Programming Theory and Practice X, pp. 31–44. Springer (2013)

    Google Scholar 

  12. Heidari, A.A., Pahlavani, P.: An efficient modified grey wolf optimizer with lévy flight for optimization tasks. Appl. Soft Comput. 60, 115–134 (2017)

    Article  Google Scholar 

  13. Houssein, E.H., Saad, M.R., Hashim, F.A., Shaban, H., Hassaballah, M.: Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell. 94, 103731 (2020)

    Article  Google Scholar 

  14. Jamil, M., Zepernick, H.J.: Lévy flights and global optimization. In: Yang, X.S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M. (eds.) Swarm Intelligence and Bio-Inspired Computation, pp. 49–72. Elsevier, Oxford (2013). https://www.sciencedirect.com/science/article/pii/B978012405163800003X

  15. Jensi, R., Jiji, G.W.: An enhanced particle swarm optimization with Lévy flight for global optimization. Appl. Soft Comput. 43, 248–261 (2016)

    Article  Google Scholar 

  16. Kaidi, W., Khishe, M., Mohammadi, M.: Dynamic Lévy flight chimp optimization. Knowl.-Based Syst. 235, 107625 (2022)

    Article  Google Scholar 

  17. Kamaruzaman, A.F., Zain, A.M., Yusuf, S.M., Udin, A.: Lévy flight algorithm for optimization problems—a literature review. In: Applied Mechanics and Materials, vol. 421, pp. 496–501. Trans Tech Publ (2013)

    Google Scholar 

  18. Khan, M.M., Ahmad, A.M., Khan, G.M., Miller, J.F.: Fast learning neural networks using cartesian genetic programming. Neurocomputing 121, 274–289 (2013)

    Article  Google Scholar 

  19. Koza, J.R., Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press (1992)

    Google Scholar 

  20. Levandowsky, M., Klafter, J., White, B.: Swimming behavior and chemosensory responses in the protistan microzooplankton as a function of the hydrodynamic regime. Bull. Mar. Sci. 43(3), 758–763 (1988)

    Google Scholar 

  21. Liu, Y., Cao, B.: A novel ant colony optimization algorithm with Lévy flight. IEEE Access 8, 67205–67213 (2020)

    Article  Google Scholar 

  22. Manazir, A., Raza, K.: Recent developments in cartesian genetic programming and its variants. ACM Comput. Surv. (CSUR) 51(6), 1–29 (2019)

    Article  Google Scholar 

  23. Mandelbrot, B.B., Mandelbrot, B.B.: The Fractal Geometry of Nature, vol. 1. WH Freeman New York (1982)

    Google Scholar 

  24. Miller, J.: Cartesian Genetic Programming, vol. 43 (2003)

    Google Scholar 

  25. Miller, J.F., Mohid, M.: Function optimization using cartesian genetic programming. In: Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation. pp. 147–148. GECCO ’13 Companion, Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2464576.2464646

  26. Miller, J.F., Thomson, P., Fogarty, T.: Designing electronic circuits using evolutionary algorithms. arithmetic circuits: a case study. Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, pp. 105–131 (1997)

    Google Scholar 

  27. Miller, J.F., et al.: An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 1135–1142 (1999)

    Google Scholar 

  28. Miller, J.F.: Cartesian genetic programming: its status and future. Genet. Program Evolvable Mach. 21(1), 129–168 (2020)

    Article  Google Scholar 

  29. Oranchak, D.: Cartesian Genetic Programming for the Java Evolutionary Computing Toolkit (CGP for ECJ) (2010). http://www.oranchak.com/cgp/doc/

  30. Reynolds, A.: Lévy flight movement patterns in marine predators may derive from turbulence cues. Proc. Roy. Soc. A: Math. Phys. Eng. Sci. 470(2171), 20140408 (2014)

    Article  Google Scholar 

  31. dos Santos Coelho, L., Bora, T.C., Klein, C.E.: A genetic programming approach based on lévy flight applied to nonlinear identification of a poppet valve. Appl. Math. Model. 38(5–6), 1729–1736 (2014)

    Article  MathSciNet  Google Scholar 

  32. Schuster, F., Levandowsky, M.: Chemosensory responses of acanthamoeba castellanii: visual analysis of random movement and responses to chemical signals. J. Eukaryot. Microbiol. 43(2), 150–158 (1996)

    Article  Google Scholar 

  33. Shlesinger, M.F., Klafter, J.: Lévy walks versus lévy flights. On Growth and Form: Fractal and Non-fractal Patterns in Physics, pp. 279–283 (1986)

    Google Scholar 

  34. Shukla, S., Kumar, L., Bera, T., Dasgupta, R.: A Lévy Flight based Narrow Passage Sampling Method for Probabilistic Roadmap Planners. arXiv preprint arXiv:2107.00817 (2021)

  35. Sotto, L.F.D.P., Kaufmann, P., Atkinson, T., Kalkreuth, R., Basgalupp, M.P.: A study on graph representations for genetic programming. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. pp. 931–939. GECCO ’20, Association for Computing Machinery, New York, NY, USA (2020), https://doi.org/10.1145/3377930.3390234

  36. Turner, A.J., Miller, J.F.: Recurrent cartesian genetic programming. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) Parallel Problem Solving from Nature—PPSN XIII, pp. 476–486. Springer International Publishing, Cham (2014)

    Chapter  Google Scholar 

  37. Viswanathan, G.M.: Fish in lévy-flight foraging. Nature 465(7301), 1018–1019 (2010)

    Article  Google Scholar 

  38. Viswanathan, G.M., Afanasyev, V., Buldyrev, S.V., Murphy, E.J., Prince, P.A., Stanley, H.E.: Lévy flight search patterns of wandering albatrosses. Nature 381(6581), 413–415 (1996)

    Article  Google Scholar 

  39. Viswanathan, G., Afanasyev, V., Buldyrev, S.V., Havlin, S., Da Luz, M., Raposo, E., Stanley, H.E.: Lévy flights in random searches. Phys. A 282(1–2), 1–12 (2000)

    Article  Google Scholar 

  40. Walker, J.A., Völk, K., Smith, S.L., Miller, J.F.: Parallel evolution using multi-chromosome cartesian genetic programming. Genet. Program Evolvable Mach. 10(4), 417 (2009)

    Article  Google Scholar 

  41. Zhou, Y., Ling, Y., Luo, Q.: Lévy flight trajectory-based whale optimization algorithm for engineering optimization. Eng. Comput. (2018)

    Google Scholar 

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Authors and Affiliations

  1. University of Oldenburg, 26129, Oldenburg, Germany

    Jörg Bremer & Sebastian Lehnhoff

Authors
  1. Jörg Bremer
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  2. Sebastian Lehnhoff
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Correspondence to Jörg Bremer .

Editor information

Editors and Affiliations

  1. Aston University, Birmingham, UK

    Nitin Naik

  2. Cardiff Metropolitan University, Cardiff, UK

    Paul Jenkins

  3. Aston University, Birmingham, UK

    Paul Grace

  4. Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  5. Devi Ahilya University, Indore, India

    Shaligram Prajapat

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Bremer, J., Lehnhoff, S. (2024). Hybridizing Lévy Flights and Cartesian Genetic Programming for Learning Swarm-Based Optimization. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_24

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