  Learning classifier systems are adaptive learning systems which have been
widely applied in a multitude of application domains. However, there are still
some generalization problems unsolved. The hurdle is that fitness and niching
pressures are difficult to balance. Here, a new algorithm called Self
Organizing Classifiers is proposed which faces this problem from a different
perspective. Instead of balancing the pressures, both pressures are separated
and no balance is necessary. In fact, the proposed algorithm possesses a
dynamical population structure that self-organizes itself to better project the
input space into a map. The niched fitness concept is defined along with its
dynamical population structure, both are indispensable for the understanding of
the proposed method. Promising results are shown on two continuous multi-step
problems. One of which is yet more challenging than previous problems of this
class in the literature.

Neural and Evolutionary Computing;Artificial Intelligence;Machine Learning;Multiagent Systems