Abstract
Community formation has certainly gained more and more attention from both the researchers and practitioners in the field of complex networks. An efficient algorithm is needed since the number of the possible communities is exponential in the number of agents. Genetic algorithm is a very useful tool for obtaining high quality and optimal solutions for optimization problems, due to its self-organization, self-adaptation and parallelism. The paper proposes a high performance genetic algorithm for community formation. The key concept in our algorithm is a new fitness index, which aims at being a trade-off between intelligence and cooperation, and allows not only community formation but also intelligence to be driving principle in the community formation process.
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Scarlat, E., Maries, I. (2011). A Genetic Algorithm for Community Formation based on Collective Intelligence Capacity. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_29
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DOI: https://doi.org/10.1007/978-3-642-22000-5_29
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