Olfactory signal classification based on evolutionary computation

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 subpopulations of solutions. Subpopulations co-evolve and converge towards different (sub-)optimal problem solutions. Only local chromosome interactions are allowed in order to avoid migration between subpopulations approximating different optimum points and to prevent the destruction of subpopulations. 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.