On cooperation between evolutionary algorithms and other search paradigms

We present a multi-agent based approach for achieving cooperation between search systems employing different search paradigms. The search agents periodically interrupt their search, select interesting information from their states that is transmitted to the other agents, filter the information sent to them with respect to their own demands, integrate the remaining information into their search, and then continue the search. There are different kinds of information to be exchanged and the selection is both success- and demand-driven. We demonstrate the usefulness of this approach by coupling a search system based on a genetic algorithm and a branch-and-bound based system for job-shop-scheduling. Our experiments show that the cooperation results in finding better solutions within a given time limit and in finding solutions comparable to those generated by the best system working alone in less time. The speed-up factors for some examples even exceed the number of agents (computers) used.