Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Classifier systems and genetic algorithms
Artificial Intelligence
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A neural network classifier for OCR using structural descriptions
Machine Vision and Applications
Structural indexing for character recognition
Computer Vision and Image Understanding
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Handwritten Numeral Recognition by Means of Evolutionary Algorithms
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Implicit niching in a learning classifier system: Nature's way
Evolutionary Computation
ADA'04 Proceedings of the 3rd international conference on Astronomical Data Analysis
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In the framework of an evolutionary approach to machine learning, this paper presents the preliminary version of a learning system that uses Genetic Programming as a tool for automatically inferring the set of classification rules to be used by a hierarchical handwritten character recognition system. In this context, the aim of the learning system is that of producing a set of rules able to group character shapes, described by using structural features, into super-classes, each corresponding to one or more actual classes. In particular, the paper illustrates the structure of the classification rules and the grammar used to generate them, the genetic operators devised to manipulate the set of rules and the fitness function used to match the current set of rules against the sample of the training set. The experimental results obtained by using a set of 5,000 digits randomly extracted from the NIST database are eventually reported and discussed.