Abstract
During the last decade, both evolutionary computation and multi-agent systems have been used for solving decision and optimization problems. This paper proposes a new evolutionary agent system by incorporating evolu-tionary process into agent concepts for solving mathematical programming models. Each of the agents represents a candidate solution of the problem, and able to sense and act on the society. The fitness of the agent improves through co-evolutionary adaptation of society with the individual learning of the agents. The performance of the proposed algorithm is tested on five new benchmark problems along with existing 13 well-known problems, and the experimental results show convincing performance.
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Ullah, A.S.S.M.B., Sarker, R., Cornforth, D. (2007). An Evolutionary Agent System for Mathematical Programming. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_20
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DOI: https://doi.org/10.1007/978-3-540-74581-5_20
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