Action-selection and crossover strategies for self-modeling machines

In previous work [7] a computational framework was demonstrated that employs evolutionary algorithms to automatically model a given system. This is accomplished by alternating the evolution of models with the evolutionary search for new training data. Theory predicts [23] that the best new training data is that which induces maximum disagreement across the current model set. Here it is demonstrated that in a robot application this is not the case, and alternative fitness functions are developed that seek other, better training data. Also, it is shown that although crossover successfully reduces the mean error of the model set, it compromises the ability of the framework to find new, informative training data. This has implications for how to create adaptive, self-modeling machines, and suggests how competitive processes in the brain underlie the generation of intelligent behavior.