Present paper introduces a new evolutionary technique for
multimodal real-valued optimization which uses a clustering method for
separating the individuals within a population into species that are each
connected to di®erent optima from the search space. It is applied for a
set of benchmark functions both for uni- and multimodal optimization
and it proves to be very e±cient as regards both the accuracy of the
obtained results and the costs regarding the —tness evaluation calls that
are spent.