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A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity

  • S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing
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Abstract

Genetic algorithm (GA) is an important and effective method to solve the optimization problem, which has been widely used in most practical applications. However, the premature convergence of GA has unexpected effect on the algorithm’s performance, the main reason is that the evolution of outstanding individuals multiply rapidly will lead to premature loss of population’s diversity. To solve the above problem, a method to qualify the population diversity and similarity between adjacent generations is proposed. Then, according to the evaluation of population diversity and the fitness of individual, the adaptive adjustment of crossover and mutation probability is realized. The results of several benchmark functions show that the proposed algorithm can search the optimal solution of almost all benchmark functions and effectively maintain the diversity of the population. Compared with the existing algorithms, it has greatly improved the convergence speed and the global optimal solution.

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References

  1. Choubey NS, Kharat MU (2011) Approaches for handling premature convergence in CFG induction using GA. Soft Comput Ind Appl 96:55–66

    Google Scholar 

  2. Potter MA, De Jong KA (1994) A cooperative coevolutionary approach to function optimization. In: Proceedings of the parallel problem solving from Nature-PPSN III, international conference on evolutionary computation. LNCS 866. Springer, Berlin, pp 249–257

  3. Pandey HM, Chaudhary A, Mehrotra D (2014) A comparative review of approaches to prevent premature convergence in GA. Appl Soft Comput 24:1047–1077

    Article  Google Scholar 

  4. Wang H, Yang S, Ip W, Wang D (2009) Adaptive primal–dual genetic algorithms in dynamic environments. IEEE Trans Syst Man Cybern Part B Cybern 39(6):1348–1361

    Article  Google Scholar 

  5. Ramadan SZ (2013) Reducing premature convergence problem in genetic algorithm: application on travel salesman problem. Comput Inf Sci 6(1):47–57

    Google Scholar 

  6. Srinivas M, Patnaik L, Patnaik M (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667

    Article  Google Scholar 

  7. Mohammadi Ivatloo B, Rabiee A, Soroudi A (2013) Nonconvex dynamic economic power dispatch problems solution using hybrid immune-genetic algorithm. IEEE Syst J 7(4):777–785

    Article  Google Scholar 

  8. Messelis T, Causmaecker PD (2014) An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem. Eur J Oper Res 233(3):511–528

    Article  MathSciNet  MATH  Google Scholar 

  9. Hamidreza E, Geiger CD (2008) A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems. J Heuristics 14(3):203–241

    Article  MATH  Google Scholar 

  10. Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35(10):3202–3212

    Article  MATH  Google Scholar 

  11. Arabali A, Ghofrani M, Etezadi-Amoli M et al (2013) Genetic-algorithm-based optimization approach for energy management. IEEE Trans Power Deliv 28(1):162–170

    Article  Google Scholar 

  12. Dai XM (2011) Allele gene based adaptive genetic algorithm to the code design. IEEE Trans Commun 59(5):1253–1258

    Article  Google Scholar 

  13. Zamani R (2013) A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem. Eur J Oper Res 229:552–559

    Article  MathSciNet  MATH  Google Scholar 

  14. Lova A, Tormos P, Cervantes M et al (2009) An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes. Int J Prod Econ 117:302–316

    Article  Google Scholar 

  15. Uzor CJ, Gongora M, Coupland S, Passow BN (2014) Real-world dynamic optimization using an adaptive-mutation compact genetic algorithm. In: Proceedings of 2014 IEEE symposium on computational intelligence in dynamic and uncertain environments (CIDUE), pp 17–23

  16. Chen H, Cui DW, Cui YA et al (2010) Ethnic group evolution algorithm. J Softw 21(5):978–990

    Article  MATH  Google Scholar 

  17. Ling Zhang, Bo Zhang (2001) Good point set based genetic algorithm. Chin J Comput 24(9):917–922

    MathSciNet  Google Scholar 

  18. Meng W, Han X, Hong B (2006) Bee evolutionary genetic algorithm. Acta Electron Sin 34(7):1294–1300

    Google Scholar 

  19. Jiang ZY, Cai ZX, Wang Y (2010) Hybrid self-adaptive orthogonal genetic algorithm for solving global optimization problems. J Softw 21(6):1296–1307

    Article  MATH  Google Scholar 

  20. Danoy G, Bouvry P, Martins T (2006) Hlcga: a hybrid competitive coevolutionary genetic algorithm. In: Proceedings of the 6th international conference on hybrid intelligent systems. Computer Society Press, pp 48–51

  21. Zhou Q, Luo WJ (2010) A novel multi-population genetic algorithm for multiple-choice multidimensional knapsack problems. In: Proceedings of the 5th international symposium on advances in computation and intelligence. Springer, Berlin, pp 148–157

  22. Liu Q, Wang X, Fu Q et al (2012) Double elite coevolutionary genetic algorithm. J Softw 23(4):765–775

    Article  MATH  Google Scholar 

  23. Rojas Cruz JA, Pereira AGC (2013) The elitist nonhomogeneous genetic algorithm: almost sure convergence. Stat Probab Lett 83(10):2179–2185

    Article  MATH  Google Scholar 

  24. Campos VEM, Pereira AGC (2013) Modeling the genetic algorithm by a non-homogeneous Markov chain: weak and strong ergodicity. Theory Probab Appl 57(57):185–192

    Google Scholar 

  25. Hallam J, Akman O, Akman F (2010) Genetic algorithms with shrinking population size. Comput Stat 25:691–705

    Article  MathSciNet  MATH  Google Scholar 

  26. Pereira AGC, Andrade BBD (2015) On the genetic algorithm with adaptive mutation rate and selected statistical applications. Comput Stat 30(1):131–150

    Article  MathSciNet  MATH  Google Scholar 

  27. Jalali Varnamkhasti M, Lee LS, Bakar A (2015) A genetic algorithm with fuzzy crossover operator and probability. Adv Oper Res 2012:1687–9147

    Google Scholar 

  28. Sathya SS, Radhika MV (2013) Convergence of nomadic genetic algorithm on benchmark mathematical functions. Appl Soft Comput 13(5):2759–2766

    Article  Google Scholar 

  29. Im S-M, Lee J-J (2008) Adaptive crossover, mutation and selection using fuzzy system for genetic algorithms. Artif Life Robot 13(1):129–133

    Article  Google Scholar 

  30. Saitoh A, Rahimi R, Nakahara M (2014) A quantum genetic algorithm with quantum crossover and mutation operations. Quantum Inf Process 13(3):737–755

    Article  MathSciNet  MATH  Google Scholar 

  31. Hartmann S (2002) Project scheduling with multiple modes: a genetic algorithm. Naval Res Logist 49:433–448

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work is supported by the project of the First-Class University and the First-Class Discipline (No. 10301-017004011501). And we wish to thank the anonymous reviewers who helped to improve the quality of the paper.

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Correspondence to Yong Lu.

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We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Sun, N., Lu, Y. A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity. Neural Comput & Applic 31, 1435–1443 (2019). https://doi.org/10.1007/s00521-018-3438-9

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  • DOI: https://doi.org/10.1007/s00521-018-3438-9

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