Using genetic algorithms in the design of morphological filters

Morphological filters are an important class of nonlinear digital signal processing and analysis filters, which have found a range of applications, giving excellent results in areas such as noise reduction, edge detection and object recognition. However, design methods existing for these morphological filters tend to be computationally intractable or require some expert knowledge of mathematical morphology. This paper demonstrates how simple genetic algorithms can be employed in the search for optimum morphological filters for specific signal/image processing tasks