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

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.

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