Implicit Niching in a Learning Classifier System: Nature's Way

We approach the difficult task of analyzing the complex behavior of even the simplest learning classifier system (LCS) by isolating one crucial subfunction in the LCS learning algorithm: covering through niching. The LCS must maintain a population of diverse rules that together solve a problem (e.g., classify examples). To maintain a diverse population while applying the GA's selection operator, the LCS must incorporate some kind of niching mechanism. The natural way to accomplish niching in an LCS is to force competing rules to share resources (i.e., rewards). This implicit LCS fitness sharing is similar to the explicit fitness sharing used in many niched GAS. Indeed, the LCS implicit sharing algorithm can be mapped onto explicit fitness sharing with a one-to-one correspondence between algorithm components. This mapping is important because several studies of explicit fitness sharing, and of niching in GAS generally, have produced key insights and analytical tools for understanding the interaction of the niching and selection forces. We can now bring those results to bear in understanding the fundamental type of cooperation (a.k.a. weak cooperation) that an LCS must promote.