Frontiers of Computer Science

, Volume 12, Issue 5, pp 950–965 | Cite as

Polygene-based evolutionary algorithms with frequent pattern mining

  • Shuaiqiang Wang
  • Yilong Yin
Research Article


In this paper, we introduce polygene-based evolution, a novel framework for evolutionary algorithms (EAs) that features distinctive operations in the evolutionary process. In traditional EAs, the primitive evolution unit is a gene, wherein genes are independent components during evolution. In polygene-based evolutionary algorithms (PGEAs), the evolution unit is a polygene, i.e., a set of co-regulated genes. Discovering and maintaining quality polygenes can play an effective role in evolving quality individuals. Polygenes generalize genes, and PGEAs generalize EAs. Implementing the PGEA framework involves three phases: (I) polygene discovery, (II) polygene planting, and (III) polygene-compatible evolution. For Phase I, we adopt an associative classification-based approach to discover quality polygenes. For Phase II, we perform probabilistic planting to maintain the diversity of individuals. For Phase III, we incorporate polygene-compatible crossover and mutation in producing the next generation of individuals. Extensive experiments on function optimization benchmarks in comparison with the conventional and state-of-the-art EAs demonstrate the potential of the approach in terms of accuracy and efficiency improvement.


polygenes evolutionary algorithms function optimization associative classification data mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors would like to thank Prof. Xin Yao for discussions and advice on this manuscript. This research was supported in part by the NSFC Joint Fund with Guangdong of China under Key Project (U1201258), the National Natural Science Foundation of China (Grant Nos. 71402083, 61573219, 61502258) and the National Science Foundation of Shandong Province (ZR2014FQ007).

Supplementary material

11704_2016_6104_MOESM1_ESM.ppt (550 kb)
Supplementary material, approximately 228 KB.


  1. 1.
    Wang S Q, Gao B J, Wang S L, Cao G B, and Yin Y L. Polygene-based evolution: a novel framework for evolutionary algorithms. In: Proceedings of the 21st ACM Conference on Information and Knowledge Management. 2012, 2263–2266Google Scholar
  2. 2.
    Boughanem M, Tamine L. Query optimization using an improved genetic algorithm. In: Proceedings of the 9th International Conference on Information and Knowledge Management. 2000, 368–373Google Scholar
  3. 3.
    Venkatraman S, Yen G G. A generic framework for constrained optimization using genetic algorithms. IEEE Transactions on Evolutionary Computation, 2005, 9(4): 424–435CrossRefGoogle Scholar
  4. 4.
    Malek H, Ebadzadeh M M, Rahmati M. Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm. Applied Intelligence, 2012, 37(2): 280–289CrossRefGoogle Scholar
  5. 5.
    Zafra A, Ventura S. Multi-objective genetic programming for multiple instance learning. In: Proceedings of the 18th European Conference on Machine Learning. 2007, 790–797Google Scholar
  6. 6.
    Chang D X, Zhang X D, Zheng CW. A genetic algorithm with gene rearrangement for k-means clustering. Pattern Recognition, 2009, 42(7): 1210–1222CrossRefGoogle Scholar
  7. 7.
    Özyer T, Alhajj R. Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer. Applied Intelligence, 2009, 31(3): 318–331CrossRefGoogle Scholar
  8. 8.
    Wang S Q, Ma J, and Liu J M. Learning to rank using evolutionary computation: Immune programming or genetic programming? In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. 2009, 1879–1882Google Scholar
  9. 9.
    Kaya M Alhajj R. Utilizing genetic algorithms to optimize membership functions for fuzzy weighted association rules mining. Applied Intelligence, 2006, 24(1): 7–15CrossRefGoogle Scholar
  10. 10.
    Weale T, Seitzer J. EVOC: a music generating system using genetic algorithms. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence. 2003, 1383–1384Google Scholar
  11. 11.
    Bryden K M, Ashlock D A, Corns S M, Willson S J. Graph-based evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2006, 10(5): 550–567CrossRefGoogle Scholar
  12. 12.
    Ishibuchi H, Tsukamoto N, Nojima Y. Diversity improvement by nongeometric binary crossover in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 2010, 14(6): 985–998CrossRefGoogle Scholar
  13. 13.
    Qu B Y, Suganthan P N, and Liang J J. Differential evolution with neighborhood mutation for multimodal optimization. IEEE Transactions on Evolutionary Computation, 2012, 16(5): 601–614CrossRefGoogle Scholar
  14. 14.
    Mabu S, Hirasawa K, Hu J L. A graph-based evolutionary algorithm: genetic network programming (GNP) and its extension using reinforcement learning. Evolutionary Computation, 2007, 15(3): 369–398CrossRefGoogle Scholar
  15. 15.
    Hu T, Chen Y Z P, Banzhaf W. WiMAX network planning using adaptive-population-size genetic algorithm. In: Proceedings of the International Conference on Applications of Evolutionary Computation. 2010, 31–40CrossRefGoogle Scholar
  16. 16.
    Zhang J, Chung H S H, Lo W L. Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Transactions on Evolutionary Computation, 2007, 11(3): 326–335CrossRefGoogle Scholar
  17. 17.
    Cross A D J, Myers R, Hancock E R. Convergence of a hill-climbing genetic algorithm for graph matching. Pattern Recognition, 2000, 33(11): 1863–1880CrossRefGoogle Scholar
  18. 18.
    Tantar A A, Melab N, Talbi E G. A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Computing, 2008, 12(12): 1185–1198CrossRefzbMATHGoogle Scholar
  19. 19.
    Chen Y P, Goldberg D E. Introducing start expression genes to the linkage learning genetic algorithm. In Proceedings of the 7th International Conference on Parallel Problem Solving from Nature. 2002, 351–360Google Scholar
  20. 20.
    Chen Y P, Peng W C, Jian M C. Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2007, 37(6): 1460–1470CrossRefGoogle Scholar
  21. 21.
    Goldman B W, Tauritz D R. Linkage tree genetic algorithms: Variants and analysis. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation Conference. 2012, 625–632Google Scholar
  22. 22.
    Shao K Y, Li F, Jiang B Y, Wang N, Zhang H Y, Li W C. Neural network optimization based on improved diploidic genetic algorithm. In: Proceedings of the International Conference on Machine Learning and Cybernetics. 2010, 1470–1475Google Scholar
  23. 23.
    Manjari K M, Gallagher M. Variable screening for reduced dependency modelling in gaussian-based continuous estimation of distribution algorithms. In: Proceedings of IEEE Congress on Evolutionary Computation. 2012, 1–8Google Scholar
  24. 24.
    Rastegar R. On the optimal convergence probability of univariate estimation of distribution algorithms. Evolutionary Computation, 2011, 19(2): 225–248CrossRefGoogle Scholar
  25. 25.
    Lawrence E. Henderson’s Dictionary of Biology. New York: Pearson/Prentice Hal, 2005Google Scholar
  26. 26.
    Lewis R. Human Genetics: Concepts and Applications. New York: McGraw Hill, 2002Google Scholar
  27. 27.
    Mather K M, Jinks J L. Biometrical Genetics. 3rd ed. London: Chapman and Hall, 1982CrossRefzbMATHGoogle Scholar
  28. 28.
    Beurton P J, Falk R, and Rheinberger H J. The Concept of the Gene in Development and Evolution. Cambridge: Cambridge University Press, 2000CrossRefGoogle Scholar
  29. 29.
    Gilbert S F. Developmental Biology. 6th ed. Sunderland, MA: Sinauer Associates, 2000Google Scholar
  30. 30.
    Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 1993, 207–216Google Scholar
  31. 31.
    Liu B, Hsu W, Ma Y M. Integrating classification and association rule mining. In: Proceedings of the 4th ACM SIGKDD International Conference on Knowledge Discovery in Databases. 1998, 443–447Google Scholar
  32. 32.
    Holland J H. Adaptation in Natural and Artificial Systems. Cambridge, MA: The MIT Press, 1975Google Scholar
  33. 33.
    Han J W, Pei J, Yin Y W. Mining frequent patterns without candidate generation. In: Proceeding of ACM SIGMOD International Conference on Management of Data. 2000, 1–12Google Scholar
  34. 34.
    Agarwal R, Aggarwal C C, Prasad V V V. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing, 2001, 61(3): 350–371CrossRefzbMATHGoogle Scholar
  35. 35.
    Hämäläinen W. Statapriori: an efficient algorithm for searching statistically significant association rules. Knowledge and Information Systems, 2010, 23(3): 373–399CrossRefGoogle Scholar
  36. 36.
    Beil F, Ester M, Xu X W. Frequent term-based text clustering. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery in Databases. 2002, 436–442Google Scholar
  37. 37.
    Leung CW, Chan S C, Chung F L. A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowledge and Information Systems, 2006, 10(3): 357–381CrossRefGoogle Scholar
  38. 38.
    Yan X F, Yu P S, Han J W. Graph indexing: a frequent structure-based approach. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2004, 335–346Google Scholar
  39. 39.
    Teredesai A, Ahmad M A, Kanodia J, Gaborski R S. Comma: a framework for integrated multimedia mining using multi-relational associations. Knowledge and Information Systems, 2006, 10(2): 135–162CrossRefGoogle Scholar
  40. 40.
    Punin J, Krishnamoorthy M, Zaki M. Web Usage Mining: Languages and Algorithms. Berlin: Springer-Verlag, 2001zbMATHGoogle Scholar
  41. 41.
    Liu C, Fei L, Yan X F, Han J W, Midkiff S P. Statistical debugging: a hypothesis testing-based approach. IEEE Transactions on Software Engineering, 2006, 32(10): 831–848CrossRefGoogle Scholar
  42. 42.
    Han J W, Cheng H, Xin D, Yan X F. Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, 2007, 15(1): 55–86MathSciNetCrossRefGoogle Scholar
  43. 43.
    Dong G Z, Li J Y. Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery in Databases. 1999, 43–52Google Scholar
  44. 44.
    Li J Y, Dong G Z, Ramamohanarao K. Making use of the most expressive jumping emerging patterns for classification. In: Proceeding of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2000, 131–145Google Scholar
  45. 45.
    Li W M, Han J W, Pei J. CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceeding of the International Conference on Data Mining. 2001, 369–376Google Scholar
  46. 46.
    Yin X X, Han J W. CPAR: classification based on predictive association rules. In: Proceeding of SIAM International Conference on Data Mining. 2003, 331–335Google Scholar
  47. 47.
    Cong G. Mining top-k covering rule groups for gene expression data. In Proceedings of the 24th ACM SIGMOD International Conference on Management of Data. 2005, 670–681Google Scholar
  48. 48.
    Ting C K, ZengWM, Lin T C. Linkage discovery through data mining. IEEE Computational Intelligence Magazine, 2010, 5(1): 10–13CrossRefGoogle Scholar
  49. 49.
    Chen Y P, Goldberg D E. Convergence time for the linkage learning genetic algorithm. Evolutionary Computation, 2005, 13(3): 279–302CrossRefGoogle Scholar
  50. 50.
    Ng K P, Wong K C. A new diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proceedings of the 6th International Conference on Genetic Algorithms. 1995, 159–166Google Scholar
  51. 51.
    Baluja S. Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163. 1994Google Scholar
  52. 52.
    Tang K, Yao X, Suganthan P N, MacNish C, Chen Y P, Chen C M, Yang Z. Benchmark functions for the CEC’ 2008 special session and competition on large scale global optimization. Technical Report. 2007Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Center of Big Data ApplicationsQilu University of TechnologyJinanChina
  2. 2.Department of Computer Science and Information SystemsUniversity of JyvaskylaJyvaskylaFinland
  3. 3.School of Computer Science and TechnologyShandong UniversityJinanChina

Personalised recommendations