Neural Computing and Applications

, Volume 31, Issue 1, pp 171–188 | Cite as

Feature selection via a novel chaotic crow search algorithm

  • Gehad Ismail SayedEmail author
  • Aboul Ella Hassanien
  • Ahmad Taher Azar
Original Article


Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.


Crow search algorithm Feature selection Optimization algorithm Chaos theory 


Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Abdullah A, Enayatifa R, Lee M (2012) A hybrid genetic algorithm and chaotic function model for image encryption. Journal of Electronics and Communication 66(1):806–816Google Scholar
  2. 2.
    Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12Google Scholar
  3. 3.
    Bache K, Lichman M UCI Machine learning repository. Retrieved July 19, 2016
  4. 4.
    Blum A, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97:245–271MathSciNetzbMATHGoogle Scholar
  5. 5.
    Cai JJ, Ma XQ, Li X (2007) Chaotic ant swarm optimization to economic dispatch. Electr Power Syst Res 77(10):1373–1380Google Scholar
  6. 6.
    Chen CH (2014) A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection. Appl Soft Comput 20:4–14Google Scholar
  7. 7.
    Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18Google Scholar
  8. 8.
    Emary E, Zawbaa H, Hassanien A (2016) Binary gray wolf optimization approaches for feature selection. Neurocomputing 172:371–381Google Scholar
  9. 9.
    Figueiredo E, Ludermir T, Bastos C (2016) Many objective particle swarm optimization. Inf Sci 374:115–134Google Scholar
  10. 10.
    Gadat S, Younes L (2007) A stochastic algorithm for feature selection in pattern recognition. Journal of Machine Learning 8:509–547zbMATHGoogle Scholar
  11. 11.
    Gai-Ge W, Suash D, Leandro D, Coelho S (2015) Elephant herding optimization 3rd international symposium on computational and business intelligence (ISCBI), Bali, pp 1–5Google Scholar
  12. 12.
    Gandomi A, Yang X, Alavi A (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89:2325–2336Google Scholar
  13. 13.
    Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98MathSciNetzbMATHGoogle Scholar
  14. 14.
    Geem Z, Kim J, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68Google Scholar
  15. 15.
    Goldberg D (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. ISBN 0201157675zbMATHGoogle Scholar
  16. 16.
    Golub TR (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537Google Scholar
  17. 17.
    Guyon I, Elisseeff A (2003) An introduction to variable and attribute selection. Machine Learning Research 3:1157–1182zbMATHGoogle Scholar
  18. 18.
    Hafez AI, Zawbaa HM, Emary E, Mahmoud HA, Hassanien AE (2015) An innovative approach for feature selection based on chicken swarm optimization 7th international conference of soft computing and pattern recognition (SoCPaR), pp 19–24Google Scholar
  19. 19.
    Hafez AI, Zawbaa HM, Emary E, Hassanien AE (2016) Sine cosine optimization algorithm for feature selection International symposium on inovations in intelligent systems and applications (INISTA), pp 1–5Google Scholar
  20. 20.
    He YY, Zhou JZ, Zhou XQ (2009) Comparison of different chaotic maps in particle swarm optimization algorithm for long term cascaded hydroelectric system scheduling. Chaos Solitons Fractals 42:3169–1376zbMATHGoogle Scholar
  21. 21.
    He YY, Zhou JZ, Li CS (2008) A precise chaotic particle swarm optimization algorithm based on improved tent map. ICNC 7:569–573Google Scholar
  22. 22.
    He Y, Zhou J, Lu N, Qin H, Lu Y (2010) Differential evolution algorithm combined with chaotic pattern search. Kybernetika 46(4):684–696MathSciNetzbMATHGoogle Scholar
  23. 23.
    Jia H, Ding S, Du M, Xue Y (2016) Approximate normalized cuts without Eigen-decomposition. Inf Sci 374:135–150Google Scholar
  24. 24.
    Jian L, Li J, Shu K, Liu H (2016) Multi-label informed feature selection Proceedings of the twenty-fifth international joint conference on artificial intelligence, pp 1627–1633Google Scholar
  25. 25.
    Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289zbMATHGoogle Scholar
  26. 26.
    Kennedy J, Eberhart R (1995) Particle swarm optimization IEEE international conference on neural networks, vol 4, pp 1942–1948Google Scholar
  27. 27.
    Kohavi R, John G (1997) Wrappers for feature subset selection. Artif Intell 97(1):273–324zbMATHGoogle Scholar
  28. 28.
    Lei Y, Huan L (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution Proceedings of the 20th international conference on machine learning (ICML-03), pp 856–863Google Scholar
  29. 29.
    Li B, Jiang W (1998) Optimizing complex functions by chaos search. Journal of Cybernetics and Systems 29:409–419zbMATHGoogle Scholar
  30. 30.
    Li X, Zhang J, Yin M (2013) Animal migration optimization: an optimization algorithm inspired by animal migration behavior, Neural Comput Applic, pages=1–11Google Scholar
  31. 31.
    Lin S, Ying KS-C, Lee Z (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35(4):1817–1824Google Scholar
  32. 32.
    Meng X, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28(4):673–687Google Scholar
  33. 33.
    Mingjun J, Tang HW (2004) Application of chaos in simulated annealing optimization. Chaos Solitons Fractals 21:933–941zbMATHGoogle Scholar
  34. 34.
    Mirjalili S, Seyed M, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61Google Scholar
  35. 35.
    Ng K, Liu H (2000) Customer retention via data mining. AI Review 14:569–590zbMATHGoogle Scholar
  36. 36.
    Repinsek M, Liu S, Mernik L (2012) A note on teaching–learning-based optimization algorithm. Inf Sci 212:79–93Google Scholar
  37. 37.
    Rui Y, Huang TS, Chang S (1999) Image retrieval: current techniques, promising directions and open issues. J Vis Commun Image Represent 10:39–62Google Scholar
  38. 38.
    Sarafrazi S (2013) Facing the classification of binary problems with a gsa-svm hybrid system. Math Comput Model 57:270–278MathSciNetzbMATHGoogle Scholar
  39. 39.
    Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimization with chaos. Neural Comput & Applic 25(5):1077–1097Google Scholar
  40. 40.
    Sayed G, Darwish A, Hassanien A, Pan S (2016) Breast cancer diagnosis approach based on meta-heuristic optimization algorithm inspired by bubble-net hunting strategy of whales 10th international conference on genetic and evolutionary computing (ICGEC), Fujian, China, pp 306–313Google Scholar
  41. 41.
    Sayed S, Nabil E, Badr A (2016) A binary clonal flower pollination algorithm for feature selection. Pattern Recogn Lett 77:21–27Google Scholar
  42. 42.
    Schiezaro M, Pedrini H (2013) Data feature selection based on artificial bee colony algorithm. EURASIP Journal on Image and Video Processing 2013(1):1–8Google Scholar
  43. 43.
    Shilaskar S, Ghatol A (2013) Feature selection for medical diagnosis: evaluation for cardiovascular diseases. Expert Syst Appl 40(10):4146–4153Google Scholar
  44. 44.
    Storn R, Price K (1997) Differential evolution -a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetzbMATHGoogle Scholar
  45. 45.
    Tavazoei MS, Haeri M (2007) Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl Math Comput 187:1076–1085MathSciNetzbMATHGoogle Scholar
  46. 46.
    Unler A, Murat A (2010) A discrete particle swarm optimization method for feature selection in binary classification problems. Journal of Operation Research 206:528–539zbMATHGoogle Scholar
  47. 47.
    Wanga G, Guo L, Gandomi A, Hao G, Wangb H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34MathSciNetGoogle Scholar
  48. 48.
    Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80–83Google Scholar
  49. 49.
    Yuan XH, Yuan YB, Zhang YC (2002) A hybrid chaotic genetic algorithm for short-term hydro system scheduling. Math Comput Simul 59(4):319–327MathSciNetzbMATHGoogle Scholar
  50. 50.
    Xiang T, Liao XF, Wong KW (2007) Comparison of different chaotic maps in particle swarm optimization algorithm for long term cascaded hydroelectric system scheduling. Appl Math Comput 190:1637–1645MathSciNetGoogle Scholar
  51. 51.
    Yang DX, Li G, Cheng GD (2007) On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34:1366–1375MathSciNetGoogle Scholar
  52. 52.
    Yang Y, Pederson JO (1997) A comparative study on feature selection in text categorization Proceedings of the fourteenth international conference on machine learning, pp 412–420Google Scholar
  53. 53.
    Yu Z (2014) Hybrid clustering solution selection strategy. Pattern Recogn 47:3362–3375Google Scholar
  54. 54.
    Yuan XF, Wang YN, Wu LH (2007) Pattern search algorithm using chaos and its application. Journal of Hunan University, Natural Sciences 34(9):30–33zbMATHGoogle Scholar
  55. 55.
    Zawbaa H, Emary E, Parv B, Shaarawi M (2016) Feature selection approach based on moth-flame optimization algorithm IEEE congress on evolutionary computation, Vancouver, Canada, pp 24–29Google Scholar
  56. 56.
    Zhang H, Sun G (2002) Feature selection using tabu search method. Pattern Recogn 35:701–711zbMATHGoogle Scholar
  57. 57.
    Zhang L, Zhang CJ (2008) Hopf bifurcation analysis of some hyperchaotic systems with time-delay controllers. Kybernetika 44(1):35–42MathSciNetzbMATHGoogle Scholar
  58. 58.
    Zhang N, Ding S, Zhang J (2016) Multi layer elm-rbf for multi-label learning. Appl Soft Comput 43:535–545Google Scholar
  59. 59.
    Zhang Q, Li Z, Zhou CJ, Wei XP (2013) Bayesian network structure learning based on the chaotic particle swarm optimization algorithm. Genet Mol Res 12(4):4468–4479Google Scholar
  60. 60.
    Zhu ZL, Li SP, Yu H (2008) A new approach to generalized chaos synchronization based on the stability of the error system. Kybernetika 44(4):492–500MathSciNetzbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Gehad Ismail Sayed
    • 1
    Email author
  • Aboul Ella Hassanien
    • 1
  • Ahmad Taher Azar
    • 2
    • 3
  1. 1.Faculty of Computers and InformationCairo UniversityCairoEgypt
  2. 2.Faculty of Computers and InformationBenha UniversityBanhaEgypt
  3. 3.Nanoelectronics Integrated Systems Center (NISC)Nile UniversityGizaEgypt

Personalised recommendations