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Wave models and dynamical analysis of evolutionary algorithms

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Abstract

By drawing an analogy between the population of an evolutionary algorithm and a gas system (which we call a particle system), we first build wave models of evolutionary algorithms based on aerodynamics theory. Then, we solve the models’ linear and quasi-linear hyperbolic equations analytically, yielding wave solutions. These describe the propagation of the particle density wave, which is composed of leftward and rightward waves. We demonstrate the convergence of evolutionary algorithms by analyzing the mechanism underlying the leftward wave, and investigate population diversity by analyzing the rightward wave. To confirm these theoretical results, we conduct experiments that apply three typical evolutionary algorithms to common benchmark problems, showing that the experimental and theoretical results agree. These theoretical and experimental analyses also provide several new clues and ideas that may assist in the design and improvement of evolutionary algorithms.

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References

  1. Back T. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford: Oxford University Press, 1996

    Book  MATH  Google Scholar 

  2. Wang F, Zhang H, Li K, et al. A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inf Sci, 2018, 436–437: 162–177

    Article  MathSciNet  Google Scholar 

  3. Guo S M, Yang C C. Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Trans Evol Computat, 2015, 19: 31–49

    Article  Google Scholar 

  4. Wegener I. Methods for the analysis of evolutionary algorithms on pseudo-boolean functions. In: Evolutionary optimization. Boston: Springer, 2003. 349–369

    Chapter  Google Scholar 

  5. Beyer H G. Convergence analysis of evolutionary algorithms that are based on the paradigm of information geometry. Evolary Computat, 2014, 22: 679–709

    Article  Google Scholar 

  6. Derrac J, García S, Hui S, et al. Analyzing convergence performance of evolutionary algorithms: a statistical approach. Inf Sci, 2014, 289: 41–58

    Article  Google Scholar 

  7. Tan C J, Neoh S C, Lim C P, et al. Application of an evolutionary algorithm-based ensemble model to job-shop scheduling. J Intell Manuf, 2019, 30: 879–890

    Article  Google Scholar 

  8. Wu H, Kuang L, Wang F, et al. A multiobjective box-covering algorithm for fractal modularity on complex networks. Appl Soft Comput, 2017, 61: 294–313

    Article  Google Scholar 

  9. Goldberg D E, Segrest P. Finite Markov chain analysis of genetic algorithms. In: Proceedings of the 2nd International Conference on Genetic Algorithms, Cambridge, 1987. 1: 1

    Google Scholar 

  10. Rudolph G. Finite Markov chain results in evolutionary computation: a tour d’horizon. Fund Inform, 1998, 35: 67–89

    MathSciNet  MATH  Google Scholar 

  11. He J, Yao X. Drift analysis and average time complexity of evolutionary algorithms. Artif Intell, 2001, 127: 57–85

    Article  MathSciNet  MATH  Google Scholar 

  12. Sudholt D. A new method for lower bounds on the running time of evolutionary algorithms. IEEE Trans Evol Computat, 2013, 17: 418–435

    Article  Google Scholar 

  13. Yu Y, Qian C, Zhou Z H. Switch analysis for running time analysis of evolutionary algorithms. IEEE Trans Evol Computat, 2015, 19: 777–792

    Article  Google Scholar 

  14. Bian C, Qian C, Tang K. A general approach to running time analysis of multi-objective evolutionary algorithms. In: Proceedings of 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, 2018. 1405–1411

  15. Mori N, Yoshida J, Tamaki H, et al. A thermodynamical selection rule for the genetic algorithm. In: Proceedings of IEEE International Conference on Evolutionary Computation, Perth, 1995. 1: 188

    Google Scholar 

  16. Cornforth T W, Lipson H. A hybrid evolutionary algorithm for the symbolic modeling of multiple-time-scale dynamical systems. Evol Intel, 2015, 8: 149–164

    Article  Google Scholar 

  17. Li Y X, Zou X F, Kang L S, et al. A new dynamical evolutionary algorithm based on statistical mechanics. J Comput Sci Technol, 2003, 18: 361–368

    Article  MATH  Google Scholar 

  18. Li Y X, Xiang Z L, Xia J N. Dynamical system models and convergence analysis for simulated annealing algorithm (in Chinese). Chin J Comput, 2019, 42: 1161–1173

    Google Scholar 

  19. Li Y X, Xiang Z L, Zhang W Y. A relaxation model and time complexity analysis for simulated annealing algorithm (in Chinese). Chin J Comput, 2019. http://kns.cnki.net/kcms/detail/11.1826.TP.20190425.1042.002.html

  20. Zhou Y L. One-Dimensional Unsteady Hydrodynamics. Beijing: Science China Press, 1998

    Google Scholar 

  21. Lamb H. Hydrodynamics. Cambridge: Cambridge University Press, 1993

    MATH  Google Scholar 

  22. Gu C H, Li D Q. Mathematical Physics Equations. Beijing: People’s Education Press, 1982

    Google Scholar 

  23. Zhang Y. Expansion Waves and Shock Waves. Beijing: Peking University Press, 1983

    Google Scholar 

  24. Shi Y, Eberhart R C. Empirical study of particle swarm optimization. In: Proceedings of IEEE International Conference on Evolutionary Computation, Washington, 1999. 3: 1945–1950

    Google Scholar 

  25. Liang J J, Qu B Y, Suganthan P N. Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization. Zhengzhou University and Nanyang Technological University, Technical Report. 2013

  26. Črepinšek M, Liu S H, Mernik M. Exploration and exploitation in evolutionary algorithms. ACM Comput Surv, 2013, 45: 1–33

    Article  MATH  Google Scholar 

  27. Liu S H, Mernik M, Bryant B R. To explore or to exploit: an entropy-driven approach for evolutionary algorithms. Int J Knowledge-based Intell Eng Syst, 2009, 13: 185–206

    Article  Google Scholar 

  28. Tang K, Yang P, Yao X. Negatively correlated search. IEEE J Sel Areas Commun, 2016, 34: 542–550

    Article  Google Scholar 

  29. Ursem R K. Diversity-guided evolutionary algorithms. In: Proceedings of International Conference on Parallel Problem Solving from Nature, Berlin, 2002. 462–471

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61672391).

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Correspondence to Yuanxiang Li or Zhenglong Xiang.

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Li, Y., Xiang, Z. & Ji, D. Wave models and dynamical analysis of evolutionary algorithms. Sci. China Inf. Sci. 62, 202101 (2019). https://doi.org/10.1007/s11432-018-9852-8

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  • DOI: https://doi.org/10.1007/s11432-018-9852-8

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