Self Organizing Classifiers: First Steps in Structured Evolutionary Machine Learning

Noname manuscript No. (will be inserted by the editor) Abstract Learning classifier systems are evolutionary machine learning algorithms, flexible enough to be applied toreinforcement, supervised and unsupervised learning problems with good performance. Recently, self organizing classifierswere proposed which are similar to learning classifier systems but have the advantage that in its structured population no balance between niching and fitness pressure is necessary. However, more tests and analysis are required to verify its benefits. Here, a variation of the first algorithm is proposed which uses a parameterless self organizing map (SOM). This algorithm isapplied in challenging problems such as big, noisy as well as dynamically changing continuous inputaction mazes(growing and compressing mazes are included) withgood performance. Moreover, a genetic operator is proposed which utilizes the topological information ofthe SOM's population structure, improving the results. Thus, the first steps in structured evolutionary machinelearning are shown, nonetheless, the problems faced are more difficult than the state-of-art continuous input-action multi-step ones. 1 Introduction Learning Classifier Systems (LCS) are several algorithms inspired by evolution [29],[20]. Different from most reinforcement learning algorithms, however, LCS algorithms do not use state-action lookup tables to predict payoff. In this manner, the difficulties that arrive from complex problems, wherea large number of states and/or actions are required, can be avoided. Oneway of solving this problem is to separate a fitness defined on a niche from fitnesses defined on other niches (i.e., having a good fitness on other niches would not influence the present niche).