Modulation identification using combined classifiers and co-evolution of classifiers and tests

Nowadays, there are considerable attentions to combined classifier. Recently, the focus has been shifting from practical heuristic solutions of combination methods to give a methodological way of design. In this study a co-evolutionary algorithm is presented for this purpose. The algorithm synthesizes an explicit classifier directly from bserved data produced by intelligently generated tests. The algorithm is composed of two co-evolving populations; one population evolves candidate classifiers. The second population evolves informative tests that either extract new information from the pattern or elicit desirable behavior from it The fitness of candidate classifiers is their ability to classify in response to all tests carried out so far; the fitness of candidate tests is their ability to make the classifiers disagree in their classifications. The generality of this modeling-evaluation algorithm is demonstrated by applying the chosen classifier of this algorithm to identify modulation methods and results depict the power of this algorithm.