Memetic Computing

, Volume 8, Issue 3, pp 189–210 | Cite as

Non-revisiting genetic algorithm with adaptive mutation using constant memory

  • Yang Lou
  • Shiu Yin Yuen
Regular Research Paper


The continuous non-revisiting genetic algorithm (cNrGA) uses the entire search history and parameter-less adaptive mutation to significantly enhance search performance. Storing the entire search history is natural and costs little when the number of fitness evaluations is small or moderate. However, if the number of evaluations required is substantial, some memory management is desirable. In this paper, we propose two pruning mechanisms to keep the memory used constant. They are least recently used pruning and random pruning. The basic idea is to prune a unit of memory when the memory threshold is reached and some new search information is required to be stored, thus keeping the overall memory used constant. Meanwhile, both pruning strategies naturally form parameter-less adaptive mutation operators. A study is carried out to evaluate the impact on performance caused by loss of search history information. Experimental results show that (1) both strategies can maintain the performance of cNrGA, up to the empirical limit when 90 % of the search history is not recorded, (2) cNrGA and its variants with constant memory outperform the real-coded genetic algorithm and the standard particle swarm optimization. By pre-extracting all the current prune-able history information and storing them into a list, namely, to-prune-list, the overhead of both pruning strategies becomes small. This suggests that cNrGA can be extended to use in situations when the number of fitness evaluations is much larger than before with no significant effect on statistical performance. This widens the applicability of cNrGA to include more practical problems that require larger number of fitness evaluations before converging to the global optima.


Non-revisiting genetic algorithms  Least recently used pruning Random pruning  Binary space partition tree 



The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 125313). We thank Dr. Chi Kin Chow for suggesting that pruning can be done randomly on the discrete version of NrGA.


  1. 1.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  2. 2.
    Davis L (ed) (1991) Handbook of genetic algorithms, vol 115. Van Nostrand Reinhold, New YorkGoogle Scholar
  3. 3.
    Friedrich T, Hebbinghaus N, Neumann F (2007) Rigorous analyses of simple diversity mechanisms. In: Proceedings of the 9th annual conference on genetic and evolutionary computation (GECCO). ACM, New York, pp 1219–1225Google Scholar
  4. 4.
    Ronald S (1998) Duplicate genotypes in a genetic algorithm. In: Proceedings of the IEEE world congress on computational intelligence (WCCI), pp 793–798Google Scholar
  5. 5.
    Povinelli RJ, Feng X (1999) Improving genetic algorithms performance by hashing fitness values. In: Proceedings of the artificial neural networks in engineering (ANNIE), pp 399–404Google Scholar
  6. 6.
    Kratica J (1999) Improving performances of the genetic algorithm by caching. Comput Artif Intell 18(3):271–283zbMATHGoogle Scholar
  7. 7.
    Yuen SY, Chow CK (2009) A genetic algorithm that adaptively mutates and never revisits. IEEE Trans Evol Comput 13(2):454–472CrossRefGoogle Scholar
  8. 8.
    Glover F, Laguna M (1997) Tabu search. Kluwer, NorwellCrossRefzbMATHGoogle Scholar
  9. 9.
    Chow CK, Yuen SY (2010) Continuous non-revisiting genetic algorithm with random search space re-partitioning and one-gene-flip mutation. In: Proceedings of the IEEE congress on evolutionary computation (CEC) , Barcelona. doi: 10.1109/CEC.2012.6252926
  10. 10.
    Chow CK, Yuen SY (2012) Continuous Non-revisiting Genetic Algorithm with Overlapped Search Sub-region. In Proceedings of the IEEE congress on evolutionary computation (CEC), Brisbane, QLD, p 1–8. doi: 10.1109/CEC.2010.5586046
  11. 11.
    Yuen SY, Chow CK (2008) A non-revisiting simulated annealing algorithm. In: Proceedings of the IEEE congress on evolutionary computation (CEC), pp 1886–1892Google Scholar
  12. 12.
    Chow CK, Yuen SY (2008) A non-revisiting particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation (CEC), pp 1879–1885Google Scholar
  13. 13.
    Du J, Rada R (2012) Memetic algorithms, domain knowledge, and financial investing. Memet Comput 4(2):109–125CrossRefGoogle Scholar
  14. 14.
    Young CN, LeBrese C, Zou JJ, Leo CJ (2013) A robust search paradigm with enhanced vine creeping optimization. Eng Optim 45(2):225–244MathSciNetCrossRefGoogle Scholar
  15. 15.
    Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142CrossRefGoogle Scholar
  16. 16.
    Jadon SS, Bansal JC, Tiwari R, Sharma H (2015) Accelerating artificial bee colony algorithm with adaptive local search. Memet Comput 7(3):215–230CrossRefGoogle Scholar
  17. 17.
    Gandomi AH, Yang XS (2012) Evolutionary boundary constraint handling scheme. Neural Comput Appl 21:1449–1462CrossRefGoogle Scholar
  18. 18.
    Wang Y, Li HX, Huang T, Li L (2014) Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl Soft Comput 18:232–247CrossRefGoogle Scholar
  19. 19.
    Wang Y, Wang BC, Li HX, Yen GG (2015) Incorporating objective function information into the feasibility rule for constrained evolutionary optimization. IEEE Trans Cybern (in press). doi: 10.1109/TCYB.2015.2493239
  20. 20.
    Chu W, Gao X, Sorooshian S (2011) Handling boundary constraints for particle swarm optimization in high-dimensional search space. Inf Sci 181:4569–4581CrossRefGoogle Scholar
  21. 21.
    Zambrano-Bigiarini M, Clerc M, Rojas R (2013) Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: Proceedings of the IEEE congress on evolutionary computation (CEC), pp 2337–2344Google Scholar
  22. 22.
    Hansen N (2006) The CMA evolution strategy: a comparing review. In: Proceedings on towards a new evolutionary computation, pp 75–102Google Scholar
  23. 23.
    Hansen N (2011) The CMA evolutionary strategy: a tutorial. In: Technical report. Accessed 14 June 2015
  24. 24.
    Mack CA (2011) Fifty years of Moore’s law. IEEE Trans Semicond Manuf 24(2):202–207CrossRefGoogle Scholar
  25. 25.
    Lou Y, Yuen SY (2015) Non-revisiting genetic algorithm with constant memory. In: Proceedings of the IEEE systems, man, and cybernetics (SMC), pp 1714–1719Google Scholar
  26. 26.
    Eshelman LJ, Schaffer JD (1992) Real-coded genetic algorithms and interval-schemata. In: Proceedings of the international conference on genetic algorithms (ICGA), pp 187–202Google Scholar
  27. 27.
    Lihu A, Holban S, Popescu O-A (2012) Real-valued genetic algorithms with disagreements. Memet Comput 4(4):317–325CrossRefGoogle Scholar
  28. 28.
    Heris SMK (2015) Implementation of real-coded genetic algorithm in MATLAB. Accessed 23 Aug 2015
  29. 29.
    Chow CK, Yuen SY (2011) An evolutionary algorithm that makes decision based on the entire previous search history. IEEE Trans Evol Comput 15(6):741–769CrossRefGoogle Scholar
  30. 30.
    Leung SW, Yuen SY, Chow CK (2012) Parameter control system of evolutionary algorithm that is aided by the entire search history. Appl Soft Comput 12(9):3063–3078CrossRefGoogle Scholar
  31. 31.
  32. 32.
    Liang JJ, Qu B-Y, Suganthan PN, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization. In: Technical report 2012, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou and technical report, Nanyang Technological University, SingaporeGoogle Scholar
  33. 33.
    Karafotias G, Hoogendoorn M, Eiben AE (2014) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput 19(2):167–187CrossRefGoogle Scholar
  34. 34.
    Sedgewick R (2002) Algorithms in Java, parts 1–4. Addison-Wesley, BostonGoogle Scholar
  35. 35.
    Knuth DE (1998) The art of computer programming: sorting and searching. Pearson Education, LondonGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Electronic EngineeringCity University of Hong KongHong KongChina

Personalised recommendations