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In decision support systems, it is essential to get a candidate solution fast, even if it means resorting to an approximation. This constraint introduces a scalability requirement with regard to the kind of heuristics which can be used in such systems. As execution time is bounded, these algorithms need to give better results and scale up with additional(More)
The fusion of the multi-agent paradigm with evolutionary computation yielded promising results in many optimization problems. Evolutionary multi-agent system (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asyn-chronous agents in previous EMAS(More)
— Existing solutions for agent-based systems turn out to be limited in some applications, like agent-based computing or simulations , where very large numbers of clearly defined agents interact heavily within a closed system. In those cases, fully-fledged, FIPA 1 compliant environment introduce unnecessary overhead, but simple tools fail to scale when(More)
Computing applications such as metaheuristics-based optimization can greatly benefit from multi-core architectures available on modern supercomputers. In this paper, we describe an easy and efficient way to implement certain population-based algorithms (in the discussed case, multi-agent computing system) on such runtime environments. Our solution is based(More)
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