Abstract
We present EvA2, a comprehensive metaheuristic optimization framework with emphasis on Evolutionary Algorithms. It presents a modular structure of interfaces and abstract classes for the implementation of both optimization problems and solvers. End users may choose among several layers of abstraction for an entrance point meeting their requirements on ease of use and access to extensive functionality. The EvA2 framework has been applied successfully in several academic as well as industrial cooperations and is extended continuously. It is freely available under an open source license (LGPL).
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Kronfeld, M., Planatscher, H., Zell, A. (2010). The EvA2 Optimization Framework. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_27
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DOI: https://doi.org/10.1007/978-3-642-13800-3_27
Publisher Name: Springer, Berlin, Heidelberg
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