Evolutionary/Genetic Programs (EPs) are powerful search techniques used to solve combinatorial optimization problems in many disciplines. Unfortunately, depending on the complexity of the problem, they can be very demanding in terms of computational resources. However, advances in Distributed Artificial Intelligence (DAI), Multi-Agent Systems (MAS) to be more specific, could help users to deal with this matter. In this paper we present an approach in which both technologies, EP and MAS, are combined together aiming to reduce the computational requirements, allowing a response within a reasonable period of time. This approach, called EP-MAS.Lib, is focusing on the interaction among agents in the MAS, and emphasizing on the optimization obtained by means of the evolutionary algorithm/technique. For evaluating the EP-MAS.Lib approach, the paper also presents a case study based on a problem related with the configuration of a neural network for a specific purpose.
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