Adaptive strategy operators based GA for rule discovery

  • T. ShobhaEmail author
  • R. J. Anandhi
Original Research


A new variant of genetic algorithm, which provides equal opportunity for all parent solution to produce the offspring solution, has been applied in discovery of classification rules from continuous datasets. The main objective of proposed algorithm is used to discover classification rule with three measures like accuracy, coverage (completeness) and comprehensibility, using which easily understandable, accurate and comprehensible rules can be generated. A new process has been defined to simplify the generated rules by reducing the features dimension, according to their role in the success of discovering rules. Adaptive approach for crossover and mutation operations has been applied to handle the exploration and exploitation in dynamic manner. Algorithm has been tested on UCI benchmark dataset. The results show the better classification accuracy and optimal selection of features. It is also observed that, proposed solution generates rules which are easy to handle and does not require computational machine for applications use.


Adaptive operators Classification Genetic algorithm Rule discovery 


  1. 1.
    Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, BerlinCrossRefzbMATHGoogle Scholar
  2. 2.
    Shirali A, Kordestani JK, Meybodi MR (2018) Self-adaptive multi-population genetic algorithms for dynamic resource allocation in shared hosting platforms. Genet Program Evol Mach 19:505CrossRefGoogle Scholar
  3. 3.
    Tinós R, Zhao L, Whitley D (2018) NK hybrid genetic algorithm for clustering. IEEE Trans Evol Comput. Google Scholar
  4. 4.
    Phiwhorm K, Saikaew KR (2017) A hybrid genetic algorithm with multi-parent crossover in fuzzy rule-based. Int J Mach Learn Comput 7(5):114–117CrossRefGoogle Scholar
  5. 5.
    Anushaa M, Sathiaseelanb JGR (2015) Feature selection using K-means genetic algorithm for multi-objective optimization. Procedia Comput Sci 57:1074–1080CrossRefGoogle Scholar
  6. 6.
    Elsayed SM, Sarker RA, Essam DL (2014) A new genetic algorithm for solving optimization problems. Eng Appl Artif Intell 27:57–69CrossRefGoogle Scholar
  7. 7.
    Sharma P, Sarojb (2015) Discovery of classification rules using distributed genetic algorithm. Procedia Comput Sci 46:276–284CrossRefGoogle Scholar
  8. 8.
    Cynthiya H, Anusha M, Sathiaseelan JGR (2015) Cognitive development of evolutionary algorithms in gene pattern mining. Int J Comput Sci Mob Comput 4(4):366–372Google Scholar
  9. 9.
    Yan X, Luo W, Li W, Chen W, Zhang C, Liu H (2013) An improved genetic algorithm and its application in classification. Int J Comput Sci Issues 10(1):1Google Scholar
  10. 10.
    Keshavamurthy BN, Khan AM, Toshniwal D (2012) Improved genetic algorithm based classification. Int J Comput Sci Inf 1(3):2231–5292Google Scholar
  11. 11.
    Soto W, Olaya-Benavides A (2011) A genetic algorithm for discovery of association rules. In: 30th international conference of the Chilean Computer Science Society (SCCC), pp 289–293.
  12. 12.
    Cattral R, Oppacher F, Lee Graham KJ (2009) Techniques for evolutionary rule discovery in data mining. IEEE congress on evolutionary computation, pp 1737–1744.
  13. 13.
    Jiang Y, Wang L, Chen L (2008) A hybrid dynamical evolutionary algorithm for classification rule discovery. Intell Inf Technol Appl 3:76–79. Google Scholar
  14. 14.
    Ding Q, Ding Q, Perrizo W (2008) PARM—an efficient algorithm to mine association rules from spatial data. IEEE Trans Syst Man Cybern 38(6):1513–1524. CrossRefGoogle Scholar
  15. 15.
    Li J (2006) On optimal rule discovery. IEEE Trans Knowl Data Eng 18(4):460–471. CrossRefGoogle Scholar
  16. 16.
    Lee RK, Yang YC, Chen JH, Chen YC (2018) Freeway travel time prediction by using the GA-based Hammerstein recurrent neural network. Genet Evol Comput Adv Intell Syst Comput 579:12–19. CrossRefGoogle Scholar
  17. 17.
    Huang YF, Tan TH, Chen BA (2018) A novel genetic algorithm for resource allocation optimization in device-to-device communications. Adv Intell Syst Comput 579:26–33. Google Scholar
  18. 18.
    Lin KT, Lin PH (2014) Information hiding based on binary encoding methods and crossover mechanism of genetic algorithms. Genet Evol Comput Adv Intell Syst Comput 238:203–212. CrossRefGoogle Scholar
  19. 19.
    Ueno A, Hagita N, Takubo T (2016) A niching genetic algorithm including an inbreeding mechanism for multimodal problems. Genet Evol Comput Adv Intell Syst Comput 387:71–80. CrossRefGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Department of CSEThe Oxford College of EngineeringBangaloreIndia
  2. 2.Department of ISENew Horizon College of EngineeringBangaloreIndia

Personalised recommendations