Handwritten Numeral Recognition by Means of Evolutionary Algorithms
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
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Advances in evolutionary computing
Databases for Research on Recognition of Handwritten Characters of Indian Scripts
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
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CSECS'08 Proceedings of the 7th conference on Circuits, systems, electronics, control and signal processing
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Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Engineering Applications of Artificial Intelligence
A review of clonal selection algorithm and its applications
Artificial Intelligence Review
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The work explores the potentiality of a clonal selection algorithm in pattern recognition (PR). In particular, a retraining scheme for the clonal selection algorithm is formulated for better recognition of handwritten numerals (a 10-class classification problem). Empirical study with two datasets (each of which contains about 12,000 handwritten samples for 10 numerals) shows that the proposed approach exhibits very good generalization ability. Experimental results reported the average recognition accuracy of about 96%. The effect of control parameters on the performance of the algorithm is analyzed and the scope for further improvement in recognition accuracy is discussed.