Self Organizing Classifiers and Niched Fitness
This work addresses a key bottleneck in learning classifier systems for researchers in adaptive learning, though it appears incremental as it builds on existing concepts with a new algorithmic twist.
The paper tackles the generalization problem in learning classifier systems by proposing Self Organizing Classifiers, which separates fitness and niching pressures to avoid balancing them, resulting in promising outcomes on continuous multi-step problems, including one more challenging than prior benchmarks.
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.