Coordinating Evolution: An Open, Peer-to-Peer Architecture for a Self-adapting Genetic Algorithm
In this paper we present an agent-based, peer-to-peer genetic algorithm capable of self-adaptation. We describe a preliminary architecture to that end, in which each agent is executing a local copy of a GA. using initially random parameters (currently restricted to the mutation rate for the purposes of experimentation). These GA agents are optimised themselves through the use of an evolutionary process of selection and recombination. Agents are selected according to the tness of their respective populations, and during the recombination phase they exchange individuals from their population as well as their optimisation parameters, which is what lends the system its self-adaptive properties. This allows the execution of "optimal optimisations" without the burden of tuning the evolutionary process by hand. Thanks to its parameter-less operation, our platform becomes more accessible and appealing to people outside the evolutionary computation community, and therefore a valuable tool in the eld of enterprise information systems. Initial empirical evaluation of the peer to peer architecture demonstrates better harnessing of the available resources, as well as added robustness and improved scalability.
genetic algorithms distributed computation multi-agent learning agent coordination