Raj Subbu and Arthur C. Sanderson
This paper presents a class of cooperative coevolutionary algorithms executing in a distributed information architecture consisting of coevolutionary agents, mobile software agents, and databases for superior network-efficient search and consequent distributed decision-making. In this approach to distributed decision-making, an objective function guides the concurrent search for logically interrelated information residing in databases widely distributed over a heterogeneous network environment. The information in these databases is logically interrelated due to the objective function that guides the decision-making. Application examples from the fields of manufacturing planning, intelligent internet search, and internet traffic management are utilized to highlight the applicability of this basic approach to network-efficient distributed decision-making.