Classifier systems evolving multi-agent system with distributed elitism

Classifier systems are rule based control systems for the learning of more or less complex tasks. They evolve in an autonomous way through solution without any external help. The knowledge base (the population) consists of rule sets (the individuals) randomly generated. The population evolves due to the use of a genetic algorithm. Solving complex problems with classifier systems involves problems being split into simpler versions. These simple problems need to evolve through the main complex problem, 'co-evolving' as agents in a multi-agent system. Two different conceptual approaches are used here. First is Elitism that is inspired by Darwin, distinct agents evolving and always keeping alive their best members. Second is Distributed Elitism which is a logical enhancement of Elitism where an agent's knowledge is distributed to make the whole evolve through solution. The two concepts have shown interesting experimental results but are still very different in use. Mixing them seems to be a fairly good solution.