An evolutionary algorithm based on cooperative coevolution is
applied to a classification problem, the Pima Indian diabetes
diagnosis problem. Previous cooperative coevolution algorithms
were developed for function optimization \cite{Pot94}, optimizing
agents behaviour \cite{Pan06} or modelling the behaviour of a
robot in an unknown environment \cite{Pot01}. The aim of this
paper is to integrate the cooperative approach into a learning
classifier system and use it for solving a real-world problem of
classification. To the best of our knowledge, there have been no
attempts on applying cooperative coevolution specifically to
classification. For each category of the classification problem, a
sub-population evolves specific rules using a classical genetic
algorithm. Sub-populations evolve simultaneously but
independently; cooperation between them takes place only when the
fitness of an individual in computed. Obtained experimental
results encourage further investigation.