PANIC: A Parallel Evolutionary Rule Based System

PANIC (Parallelism And Neural networks In Classifier systems) is a parallel system to evolve behavioral strategies codified by sets of rules. It integrates several adaptive techniques and computational paradigms, such as genetic algorithms, neural networks, temporal difference methods and classifier systems, to define a powerful and robust learning system. To allocate credit to rules, we propose a new mechanism, QCredit Assignment (QCA), based on the temporal difference method Qlearning. To overcome the sharing rule problem, posed by traditional credit assignment strategies in rule based systems, QCA evaluates a rule depending on the context where it is applied. The mechanism is implemented through a multi-layer, feed-forward neural network. To overcome the heavy computational load of this approach, a decentralized and asynchronous parallel model of the genetic algorithm for a massive parallel architecture has been devised.