SHAPE-ANALYSIS USING GENETIC ALGORITHMS

This paper introduces a novel methodology for shape discrimination by combining pattern recognition techniques such as morPhological processing with concepts from artificial intelligence and machine learning such as genetic algorithms (GAs). High-performance shape discrimination operators, defined as variable structuring elements and sequenced as program forms, are derived using GAs. The population of operators, iteratively evaluated according to an performance index corresponding to shape discrimination ability, evolves into an optimal set of operators using the evolutionary principles of genetic search. Experimental results are included to illustrate the feasibility of our novel methodology for developing robust shape analysis methods.