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EnglishIn this paper, we propose an evolutionary method for detecting the optimal number of clusters in a data set, and describe its application to classification of signals generated by olfactory sensors. The method is based on a new evolutionary search and optimization strategy. The strategy forces the formation and maintenance of sub-populations of solutions. Sub-populations co-evolve and converge towards different (sub-) optimal problem solutions. Only local chromosome interactions are allowed in order to avoid migration between sub-populations approximating different optimum points and to prevent the destruction of sub-populations. To this aim, specific selection and acceptance strategies have been defined. Experimental results obtained by applying the method to two test cases are also included.
| Titolo: | Olfactory signal classification based on evolutionary programming |
| Autori interni: | |
| Anno del prodotto: | 1999 |
| Abstract: | In this paper, we propose an evolutionary method for detecting the optimal number of clusters in a data set, and describe its application to classification of signals generated by olfactory sensors. The method is based on a new evolutionary search and optimization strategy. The strategy forces the formation and maintenance of sub-populations of solutions. Sub-populations co-evolve and converge towards different (sub-) optimal problem solutions. Only local chromosome interactions are allowed in order to avoid migration between sub-populations approximating different optimum points and to prevent the destruction of sub-populations. To this aim, specific selection and acceptance strategies have been defined. Experimental results obtained by applying the method to two test cases are also included. |
| Handle: | http://hdl.handle.net/11568/194289 |
| ISBN: | 0780355296 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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