This paper addresses the use of multi-objective optimization techniques for optimal zoning design in the context of handwritten digit recognition. More precisely, the Non-dominant Sorting Genetic Algorithm II (NSGA II) has been considered for the optimization of Voronoi-based zoning methods. In this case both the number of zones and the zone position and shape are optimized in a unique genetic procedure. The experimental results point out the usefulness of multi-objective genetic algorithms for achieving effective zoning topologies for handwritten digit recognition.
Handwritten Digit Recognition by Multi-Objective Optimization of Zoning Methods
IMPEDOVO, Sebastiano;PIRLO, Giuseppe;
2012-01-01
Abstract
This paper addresses the use of multi-objective optimization techniques for optimal zoning design in the context of handwritten digit recognition. More precisely, the Non-dominant Sorting Genetic Algorithm II (NSGA II) has been considered for the optimization of Voronoi-based zoning methods. In this case both the number of zones and the zone position and shape are optimized in a unique genetic procedure. The experimental results point out the usefulness of multi-objective genetic algorithms for achieving effective zoning topologies for handwritten digit recognition.File in questo prodotto:
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