Abstract
We present the application of Genetic Programming (GP) in Branch and Bound (B&B) based Mixed Integer Linear Programming (MIP). The hybrid architecture introduced employs GP as a node selection expression generator: a GP run, embedded into the B&B process, exploits the characteristics of the particular MIP problem being solved, evolving a problem-specific node selection method. The evolved method replaces the default one for the rest of the B&B. The hybrid approach outperforms depth-first and breadth-first search, and compares well with the advanced Best Projection method.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kostikas, K., Fragakis, C. (2004). Genetic Programming Applied to Mixed Integer Programming. In: Keijzer, M., O’Reilly, UM., Lucas, S., Costa, E., Soule, T. (eds) Genetic Programming. EuroGP 2004. Lecture Notes in Computer Science, vol 3003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24650-3_11
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DOI: https://doi.org/10.1007/978-3-540-24650-3_11
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