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Efficiency improvement of genetic network programming by tasks decomposition in different types of environments

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Abstract

Genetic Network Programming (GNP) is a relatively recently proposed evolutionary algorithm which is an extension of Genetic Programming (GP). However, individuals in GNP have graph structures. This algorithm is mainly used in decision making process of agent control problems. It uses a graph to make a flowchart and use this flowchart as a decision making strategy that an agent must follow to achieve the goal. One of the most important weaknesses of this algorithm is that crossover and mutation break the structures of individuals during the evolution process. Although it can lead to better structures, this may break suitable ones and increase the time needed to achieve optimal solutions. Meanwhile, all the researches in this field are dedicated to test GNP in deterministic environments. However, most of the real-world problems are stochastic and this is another issue that should be addressed. In this research, we try to find a mechanism that GNP shows better performance in stochastic environments. In order to achieve this goal, the evolution process of GNP was modified. In the proposed method, the experience of promising individuals was saved in consecutive generations. Then, to generate offspring in some predefined number of generations, the saved experiences were used instead of crossover and mutation. The experimental results of the proposed method were compared with GNP and some of its versions in both deterministic and stochastic environments. The results demonstrate the superiority of our proposed method in both deterministic and stochastic environments.

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References

  1. D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  2. J.H. Holland, Adaptation in Natural and Artificial Systems. An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence (University of Michigan Press, Ann Arbor, MI, 1975).

    MATH  Google Scholar 

  3. J.R. Koza, Genetic Programming II: Automatic Discovery of Reusable Subprograms (MA, USA, Cambridge, 1994).

    MATH  Google Scholar 

  4. J. R. Koza, Genetic programming: on the programming of computers by means of natural selection vol. 1: MIT press, 1992.

  5. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95 - International Conference on Neural Networks, 1995, pp. 1942–1948.

  6. M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. IEEE Comput. Intell. Mag. 1, 28–39 (2006)

    Article  Google Scholar 

  7. M. Dorigo, V. Maniezzo, A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics. Part B (Cybernetics) 26, 29–41 (1996)

    Article  Google Scholar 

  8. D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8, 687–697 (2008)

    Article  Google Scholar 

  9. M. Pelikan, "Probabilistic model-building genetic algorithms," presented at the Proceedings of the 10th annual conference companion on Genetic and evolutionary computation, Atlanta, GA, USA, 2008.

  10. G. Dhiman, V. Kumar, KnRVEA: A hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies for many-objective optimization. Appl. Intell. 49, 2434–2460 (2019)

    Article  Google Scholar 

  11. M. Roshanzamir, M.A. Balafar, S.N. Razavi, Empowering particle swarm optimization algorithm using multi agents’ capability: A holonic approach. Knowl.-Based Syst. 136, 58–74 (2017)

    Article  Google Scholar 

  12. M. Roshanzamir, M.A. Balafar, S.N. Razavi, A new hierarchical multi group particle swarm optimization with different task allocations inspired by holonic multi agent systems. Expert Syst. Appl. 149, 113292 (2020)

    Article  Google Scholar 

  13. L. Araujo, Genetic programming for natural language processing. Genet. Program Evolvable Mach. 21, 11–32 (2020)

    Article  Google Scholar 

  14. V. Ciesielski, Linear genetic programming. Genet. Program Evolvable Mach. 9, 105–106 (2008)

    Article  Google Scholar 

  15. N. Pillay, The impact of genetic programming in education. Genet. Program Evolvable Mach. 21, 87–97 (2020)

    Article  Google Scholar 

  16. A. Lensen, M. Zhang, B. Xue, Multi-objective genetic programming for manifold learning: balancing quality and dimensionality. Genet. Program Evolvable Mach. 21, 399–431 (2020)

    Article  Google Scholar 

  17. W. La Cava, J.H. Moore, Learning feature spaces for regression with genetic programming. Genet. Program Evolvable Mach. 21, 433–467 (2020)

    Article  Google Scholar 

  18. T. Hu, M. Tomassini, W. Banzhaf, A network perspective on genotype–phenotype mapping in genetic programming. Genet. Program Evolvable Mach. 21, 375–397 (2020)

    Article  Google Scholar 

  19. S. Mabu, K. Hirasawa, J. Hu, J. Murata, Online Learning of Genetic Network Programming. IEEJ Transactions on Electronics, Information and Systems 122, 355–362 (2002)

    Article  Google Scholar 

  20. H. Katagiri, K. Hirasawa, J. Hu, and J. Murata, "Network structure oriented evolutionary model-genetic network programming-and its Comparison with genetic programming," presented at the Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, San Francisco, California, USA, 2001.

  21. H. Katagiri, K. Hirasama, and J. Hu, "Genetic network programming - application to intelligent agents," in IEEE International Conference on Systems, Man, and Cybernetics, 2000, pp. 3829–3834 vol.5.

  22. S. Mabu, K. Hirasawa, M. Obayashi, T. Kuremoto, A variable size mechanism of distributed graph programs and its performance evaluation in agent control problems. Expert Syst. Appl. 41, 1663–1671 (2014)

    Article  Google Scholar 

  23. A. E. Eiben and J. E. Smith, Introduction to evolutionary computing vol. 53: Springer, 2003.

  24. A. E. Teller and M. Veloso, "PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System," Carnegie Mellon University1995.

  25. J. F. Miller and P. Thomson, "Cartesian Genetic Programming," Berlin, Heidelberg, 2000, pp. 121–132.

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

    Article  Google Scholar 

  27. D.B. Fogel, An introduction to simulated evolutionary optimization. IEEE Trans. Neural Networks 5, 3–14 (1994)

    Article  Google Scholar 

  28. S. Mabu, K. Hirasawa, and J. Hu, "Genetic Network Programming with Reinforcement Learning and Its Performance Evaluation," in Genetic and Evolutionary Computation Conference, GECCO, Seattle, WA, USA, June 26–30. Proceedings, Part II, K. Deb, Ed., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 710–711.

  29. T. Atkinson, D. Plump, and S. Stepney, "Evolving Graphs by Graph Programming," Cham, 2018, pp. 35–51.

  30. Q. Meng, S. Mabu, Y. Wang, and K. Hirasawa, "Guiding the evolution of Genetic Network Programming with reinforcement learning," in IEEE Congress on Evolutionary Computation, 2010, pp. 1–8.

  31. S. Mabu, K. Hirasawa, J. Hu, A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning. Evol. Comput. 15, 369–398 (2007)

    Article  Google Scholar 

  32. R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction vol. 1: MIT press Cambridge, 1998.

  33. S. Mabu, K. Hirasawa, and J. Hu, "Genetic network programming with learning and evolution for adapting to dynamical environments," in The Congress on Evolutionary Computation 2003, pp. 69–76 Vol.1.

  34. P. Sung Gil, S. Mabu, and K. Hirasawa, "Robust Genetic Network Programming using SARSA Learning for autonomous robots," in ICCAS-SICE, 2009, pp. 523–527.

  35. S. Mabu, H. Hatakeyama, K. Hirasawa, and H. Jinglu, "Genetic Network Programming with Reinforcement Learning Using Sarsa Algorithm," in IEEE International Conference on Evolutionary Computation, 2006, pp. 463–469.

  36. O. Michel, "Khepera simulator package version 2.0: Freeware mobile robot simulator written at the University of Nice-Sophia-Antipolis by Olivier Michel," Khepera Simulator version 2. 0, 1996.

  37. S. Mabu and K. Hirasawa, "Evolving plural programs by genetic network programming with multi-start nodes," in IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 1382–1387.

  38. X. Li, S. Mabu, H. Zhou, K. Shimada, and K. Hirasawa, "Genetic Network Programming with Estimation of Distribution Algorithms for class association rule mining in traffic prediction," in IEEE Congress on Evolutionary Computation, 2010, pp. 1–8.

  39. X. Li, S. Mabu, K. Hirasawa, Towards the Maintenance of Population Diversity: A Hybrid Probabilistic Model Building Genetic Network Programming. Transaction of the Japanese Society for Evolutionary Computation 1, 89–101 (2010)

    Google Scholar 

  40. X. Li, B. Li, S. Mabu, and K. Hirasawa, "A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning," in IEEE Congress of Evolutionary Computation, 2011, pp. 37–44.

  41. X. Li, S. Mabu, K. Hirasawa, A Novel Graph-Based Estimation of the Distribution Algorithm and its Extension Using Reinforcement Learning. IEEE Trans. Evol. Comput. 18, 98–113 (2014)

    Article  Google Scholar 

  42. Q. Meng, S. Mabu, and K. Hirasawa, "Genetic Network Programming with Sarsa Learning Based Nonuniform Mutation," in IEEE International Conference on Systems, Man and Cybernetics, 2010, pp. 1273–1278.

  43. X. Li, W. He, and K. Hirasawa, "Learning and evolution of genetic network programming with knowledge transfer," in IEEE Congress on Evolutionary Computation, 2014, pp. 798–805.

  44. A. T. Naeini and M. Ghaziasgar, "Improving coordination via emergent communication in cooperative multiagent systems: A Genetic Network Programming approach," in IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 589–594.

  45. A. T. Naeini and M. Palhang, "Evolving a multiagent coordination strategy using Genetic Network Programming for pursuit domain," in IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008, pp. 3102–3107.

  46. M. Benda, V. Jagannathan, R. Dodhiawala, “On Optimal Cooperation of Knowledge Sources - An Empirical Investigation,” Technical Report BCS-G2010-28 (Boeing Advanced Technology Center, Boeing Computing Services, Seattle, WA, USA, 1986).

    Google Scholar 

  47. H. Itoh, N. Ikeda, and K. Funahashi, "Heterogeneous Multi-agents Learning Using Genetic Network Programming with Immune Adjustment Mechanism," in New Advances in Intelligent Decision Technologies: Results of the First KES International Symposium IDT, K. Nakamatsu, G. Phillips-Wren, L. C. Jain, and R. J. Howlett, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 383–391.

  48. X. Li and K. Hirasawa, "Extended rule-based genetic network programming," presented at the Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, Amsterdam, The Netherlands, 2013.

  49. X. Li, M. Yang, S. Wu, Niching genetic network programming with rule accumulation for decision making: An evolutionary rule-based approach. Expert Syst. Appl. 114, 374–387 (2018)

    Article  Google Scholar 

  50. Y. Lu, Z. Jin, M. Shingo, H. Kotaro, H. Jinglu, and M. Sandor, "Elevator group control system using genetic network programming with ACO considering transitions," in SICE Annual Conference, 2007, pp. 1330–1336.

  51. Y. Lu, Z. Jin, M. Shingo, H. Kotaro, H. Jinglu, and S. Markon, "Double-deck Elevator Group Supervisory Control System using Genetic Network Programming with Ant Colony Optimization," in IEEE Congress on Evolutionary Computation, 2007, pp. 1015–1022.

  52. M. Roshanzamir, M. Palhang, A. Mirzaei, Graph structure optimization of Genetic Network Programming with ant colony mechanism in deterministic and stochastic environments. Swarm and Evolutionary Computation 51, 100581 (2019)

    Article  Google Scholar 

  53. X. Li, G. Yang, and K. Hirasawa, "Evolving directed graphs with artificial bee colony algorithm," in 14th International Conference on Intelligent Systems Design and Applications, 2014, pp. 89–94.

  54. X. Li, H. Yang, M. Yang, Revisiting Genetic Network Programming (GNP): Towards the Simplified Genetic Operators. IEEE Access 6, 43274–43289 (2018)

    Article  Google Scholar 

  55. X. Li, W. He, and K. Hirasawa, "Genetic Network Programming with Simplified Genetic Operators," in Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3–7, 2013. Proceedings, Part II, M. Lee, A. Hirose, Z.-G. Hou, and R. M. Kil, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 51–58.

  56. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach: Prentice Hall Press, 2009.

  57. M. Pollack and M. Ringuette, "Introducing the Tileworld: experimentally evaluating agent architectures," environment, pp. 183–189, 1990.

  58. F. Wilcoxon, Individual Comparisons by Ranking Methods. Biometrics Bulletin 1, 80–83 (1945)

    Article  Google Scholar 

  59. V. Nannen, S. K. Smit, and A. E. Eiben, "Costs and Benefits of Tuning Parameters of Evolutionary Algorithms," Berlin, Heidelberg, 2008, pp. 528–538.

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

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Fasa University, 74617-81189, Fasa, Iran

    Mohamad Roshanzamir

  2. Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111, Isfahan, Iran

    Maziar Palhang & Abdolreza Mirzaei

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  1. Mohamad Roshanzamir
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  2. Maziar Palhang
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  3. Abdolreza Mirzaei
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Correspondence to Mohamad Roshanzamir.

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Roshanzamir, M., Palhang, M. & Mirzaei, A. Efficiency improvement of genetic network programming by tasks decomposition in different types of environments. Genet Program Evolvable Mach 22, 229–266 (2021). https://doi.org/10.1007/s10710-021-09402-y

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