Advertisement

Best Bound Population-Based Local Search for Memetic Algorithm in View of Character Recognition

  • Rashmi WelekarEmail author
  • Nileshsingh V. ThakurEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)

Abstract

Memetic algorithms (MAs) are originally optimization algorithms with separate individual improvement, and they tend to fully exploit the problem area under consideration. But just like human brain, the recognition time tends to increase with increasing size of population. This paper aims to provide a logical solution using cultural evolution and local learning feature of MA. By introducing best bound population (BBP) from available set of population size, it is possible to keep recognition time in acceptable limits. The best bound population can be continuously upgraded using local search. The paper also revisits some popular techniques of character recognition using traditional approach and using genetic approach. Finally, all techniques are compared for error percentage and recognition time. The relative comparison with figures is presented to justify the findings.

Keywords

Memetic algorithms Best bound population Local search Genetic algorithms implementation Edit distance Connected segments Character recognition 

References

  1. 1.
    Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms model taxonomy and design issues. IEEE Trans Evol Comput 9(5):475–488CrossRefGoogle Scholar
  2. 2.
    Moscato P (1989) On evolution, search, optimization, GAs and martial arts: toward memetic algorithms. California Institute of Technology Pasadena, CA, Technical Report Caltech Concurrent Computation Program, Report 826Google Scholar
  3. 3.
    He Mort (2000) Hybrid genetic algorithms for telecommunications network back-up routing. BT Technol J 18(4):42–56CrossRefGoogle Scholar
  4. 4.
    Vazquez M, Whitley L (2000) A hybrid genetic algorithm for the quadratic assignment problem. In: Proceedings of the 2nd annual conference on genetic and evolutionary computation, pp 135–142Google Scholar
  5. 5.
    Fleurent C, Ferland J (1993) Genetic hybrids for the quadratic assignment problem. In: DIMACS, Series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, Providence, RIGoogle Scholar
  6. 6.
    Merz P (2000) Memetic algorithms for combinatorial optimization problems: fitness landscapes and effective search strategies. Ph.D. Dissertation, Parallel Systems Research Group, Department of Electrical Engineering Computer Science, University of Siegen, Siegen, GermanyGoogle Scholar
  7. 7.
    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a lamarkian genetic algorithm and an empirical binding free energy function. J Comput Chem 14:1639–1662CrossRefGoogle Scholar
  8. 8.
    Ku K, Mak M (1998) Empirical analysis of the factors that affect the Baldwin Effect. In: Proceeding Parallel Problem Solving From Nature—PPSN-V (Lecture Notes in Computer Science), pp 481–490Google Scholar
  9. 9.
    Welekar R, Thakur NV (2015) Memetic algorithm used in character recognition. In: 5th International conference, SEMCCO 2014, Bhuvaneshwar, LNCS 8947, Springer, pp 636–646Google Scholar
  10. 10.
    Sevaux M, Kenneth S (2005) Permutation distance measures for memetic algorithms with population management. In: MIC2005: The Sixth Metaheuristics International Conference, Vienna, AustriaGoogle Scholar
  11. 11.
    Altntas C, Asta S, Ozcan E, Yigit T (2014) A self-generating memetic algorithm for examination timetabling. In: 10th International conference of the practice and theory of automated timetabling, pp 26–29Google Scholar
  12. 12.
    Ye T, Wang T, Lu Z, Hao JK (2014) A multi-parent memetic algorithm for the linear ordering problem. arXiv preprint arXiv:1405.4507
  13. 13.
    Martínez-Salazar I, Molina J, Caballero R, Ángel-Bello F (2014) Memetic algorithms for solving a bi-objective transportation location routing problem. In: Proceedings of the 2014 industrial and systems engineering research conferenceGoogle Scholar
  14. 14.
    Dey N, Ashour AS, Nguyen GN Recent advancement in multimedia content using deep learningGoogle Scholar
  15. 15.
    Karaa WBA, Dey N (2017) Mining multimedia documents. CRC PressGoogle Scholar
  16. 16.
    Senior AW, Robinson AJ (1998) An offline cursive handwriting recognition system. IEEE Trans Pattern Anal Mach Intell 20(3):309–321CrossRefGoogle Scholar
  17. 17.
    Gatos B, Pratikakis I, Perantonis SJ (2006) Hybrid offline cursive handwriting word recognition. In: 18th International conference on pattern recognition (ICPR’06), pp 998–1002Google Scholar
  18. 18.
    Blumenstein M, Liu XY, Verma B (2007) A modified direction feature for cursive character recognition. Pattern Recogn 40(2):376–388CrossRefGoogle Scholar
  19. 19.
    Cheng CK, Liu XY, Blumenstein M, Marasamy VM (2004) Enhancing neural confidence based segmentation for cursive handwriting recognition. In: SEAL 04 and 2004 FIRA Robot world congressGoogle Scholar
  20. 20.
    Bozinovic RM, Shrihari SN (1989) Offline cursive script word recognition. IEEE Trans Pattern Anal Mach Intell 11(1):68–83CrossRefGoogle Scholar
  21. 21.
    Plamondan R, Shrihari SN (2000) Online and offline handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1)Google Scholar
  22. 22.
    Rodrigues RJ, Thome ACG (2000) Cursive character recognition—a character segmentation method using projection profile-based technique. In: 6th International Conference on Information System, Analysis and Synthesis—ISASGoogle Scholar
  23. 23.
    Malik L, Deshpande PS, Sandhya Bhagat (2006) Character recognition using relationship between connected segments and neural network. Wseas Trans Comput 5(1)Google Scholar
  24. 24.
    Rehman A, Saba T (2012) Off-Line cursive script recognition: current advances, comparisons and remaining problems. Artif Intell Rev 37:261–288CrossRefGoogle Scholar
  25. 25.
    Verma B, Blumenstein M (2008) Pattern recognition technologies and applications: recent advances. Information Science Reference (An Imprint of IGI Global Publications), Hershey, New York, pp 1–16Google Scholar
  26. 26.
    Alginahi Y (2010) Preprocessing techniques in character recognition, character recognition. In: Mori M (ed) InTechopen Publishers, pp 1–20, ISBN: 978-953-307-105-3Google Scholar
  27. 27.
    Minimum Edit Distance, http://www.merriampark.com/ld.htm
  28. 28.
    Karaa WBA, Ashour AS, Sassi DB, Roy P, Kausar N, Dey N (2016) Medline text mining: an enhancement genetic algorithm based approach for document clustering. In: Applications of intelligent optimization in biology and medicine. Springer International Publishing, pp 267–287Google Scholar
  29. 29.
    Smith J (2002) Genetic algorithms: simulating evolution on the computer. Part 1Google Scholar
  30. 30.
    Bazzoli A, Tettamanzi AGB (2004) A memetic algorithm for protein structure prediction in a 3D-lattice HP model. In: EvoWorkshop, LNCS3005, p 1Google Scholar
  31. 31.
    Vashist P, Hema K (2013) Character recognition with minimum edit distance method. Int J Sci Res (IJSR) 2(4) India Online ISSN: 2319‐7064Google Scholar
  32. 32.
    Arora S, Bhattacharjee D, Nasipuri M, Basu DK, Kundu M (2010) Recognition of non-compound handwritten devnagari characters using a combination of MLP and minimum edit distance. Int J Comput Sci Secur (IJCSS) 4(1)Google Scholar
  33. 33.
    Abandah GA, Jamour FT (2014) A word matching algorithm in handwritten Arabic recognition using multiple-sequence weighted edit distances. IJCSI Int J Comput Sci 11(3):18Google Scholar
  34. 34.
    Oncina Jose, Sebban Marc (2006) Learning stochastic edit distance: application in handwritten character recognition. Pattern Recogn 39:1575–1587 ElsevierCrossRefGoogle Scholar
  35. 35.
    Deshpande PS, Malik L, Arora S (2008) Fine classification & recognition of hand written devnagari characters with regular expressions & minimum edit distance method. J Comput 3(5):11–17CrossRefGoogle Scholar
  36. 36.
    Khurshid K, Faure C, Vincent N (2009) A novel approach for word spotting using merge-split edit distance. Laboratoire CRIP5—SIP, Université Paris Descartes, 45 rue des Saints-Pères, 75006, Paris, FranceGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Shri Ramdeobaba College of Engineering and ManagementNagpurIndia
  2. 2.Nagpur Institute of TechnologyNagpurIndia

Personalised recommendations