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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms model taxonomy and design issues. IEEE Trans Evol Comput 9(5):475–488
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 826
He Mort (2000) Hybrid genetic algorithms for telecommunications network back-up routing. BT Technol J 18(4):42–56
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 135–142
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, RI
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, Germany
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 14:1639–1662
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 481–490
Welekar R, Thakur NV (2015) Memetic algorithm used in character recognition. In: 5th International conference, SEMCCO 2014, Bhuvaneshwar, LNCS 8947, Springer, pp 636–646
Sevaux M, Kenneth S (2005) Permutation distance measures for memetic algorithms with population management. In: MIC2005: The Sixth Metaheuristics International Conference, Vienna, Austria
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 26–29
Ye T, Wang T, Lu Z, Hao JK (2014) A multi-parent memetic algorithm for the linear ordering problem. arXiv preprint arXiv:1405.4507
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 conference
Dey N, Ashour AS, Nguyen GN Recent advancement in multimedia content using deep learning
Karaa WBA, Dey N (2017) Mining multimedia documents. CRC Press
Senior AW, Robinson AJ (1998) An offline cursive handwriting recognition system. IEEE Trans Pattern Anal Mach Intell 20(3):309–321
Gatos B, Pratikakis I, Perantonis SJ (2006) Hybrid offline cursive handwriting word recognition. In: 18th International conference on pattern recognition (ICPR’06), pp 998–1002
Blumenstein M, Liu XY, Verma B (2007) A modified direction feature for cursive character recognition. Pattern Recogn 40(2):376–388
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 congress
Bozinovic RM, Shrihari SN (1989) Offline cursive script word recognition. IEEE Trans Pattern Anal Mach Intell 11(1):68–83
Plamondan R, Shrihari SN (2000) Online and offline handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1)
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—ISAS
Malik L, Deshpande PS, Sandhya Bhagat (2006) Character recognition using relationship between connected segments and neural network. Wseas Trans Comput 5(1)
Rehman A, Saba T (2012) Off-Line cursive script recognition: current advances, comparisons and remaining problems. Artif Intell Rev 37:261–288
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 1–16
Alginahi Y (2010) Preprocessing techniques in character recognition, character recognition. In: Mori M (ed) InTechopen Publishers, pp 1–20, ISBN: 978-953-307-105-3
Minimum Edit Distance, http://www.merriampark.com/ld.htm
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 267–287
Smith J (2002) Genetic algorithms: simulating evolution on the computer. Part 1
Bazzoli A, Tettamanzi AGB (2004) A memetic algorithm for protein structure prediction in a 3D-lattice HP model. In: EvoWorkshop, LNCS3005, p 1
Vashist P, Hema K (2013) Character recognition with minimum edit distance method. Int J Sci Res (IJSR) 2(4) India Online ISSN: 2319‐7064
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)
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):18
Oncina Jose, Sebban Marc (2006) Learning stochastic edit distance: application in handwritten character recognition. Pattern Recogn 39:1575–1587 Elsevier
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):11–17
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, France
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Welekar, R., Thakur, N.V. (2019). Best Bound Population-Based Local Search for Memetic Algorithm in View of Character Recognition. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 797. Springer, Singapore. https://doi.org/10.1007/978-981-13-1165-9_31
Download citation
DOI: https://doi.org/10.1007/978-981-13-1165-9_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1164-2
Online ISBN: 978-981-13-1165-9
eBook Packages: EngineeringEngineering (R0)