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Zmuda MA, Tamburino LA, Rizki MM. An evolutionary learning system for synthesizing complex morphological filters. IEEE Trans Syst Man Cybern B Cybern. 1996;26(4):645-53doi: 10.1109/3477.517040.
Zmuda, M. A., Tamburino, L. A., & Rizki, M. M. (1996). An evolutionary learning system for synthesizing complex morphological filters. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 26(4), 645-53. https://doi.org/10.1109/3477.517040
Zmuda, M A, et al. "An evolutionary learning system for synthesizing complex morphological filters." IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society vol. 26,4 (1996): 645-53. doi: https://doi.org/10.1109/3477.517040
Zmuda MA, Tamburino LA, Rizki MM. An evolutionary learning system for synthesizing complex morphological filters. IEEE Trans Syst Man Cybern B Cybern. 1996;26(4):645-53. doi: 10.1109/3477.517040. PMID: 18263064.
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IEEE Trans Syst Man Cybern B Cybern. 1996;26(4):645-53. doi: 10.1109/3477.517040.

An evolutionary learning system for synthesizing complex morphological filters.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society

M A Zmuda, L A Tamburino, M M Rizki

Affiliations

  1. Spectra Res., Centerville, OH.

PMID: 18263064 DOI: 10.1109/3477.517040

Abstract

This paper describes a system based on evolutionary learning, called MORPH, that semi-automates the generation of morphological programs. MORPH maintains a population of morphological programs that is continually enhanced. The first phase of each learning cycle synthesizes morphological sequences that extract novel features which increase the population's diversity. The second phase combines these newly formed operator sequences into larger programs that are better than the individual programs. A stochastic selection process eliminates the poor performers, while the survivors serve as the basis of another learning cycle. Experimental results are presented for binary and grayscale target recognition problems.

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