Abstract
We consider the dialectic search paradigm for box-constrained, non-linear optimization with heterogeneous variable types. In particular, we devise an implementation that can handle any computable objective function, including non-linear, non-convex, non-differentiable, non-continuous, non-separable and multi-modal functions. The variable types we consider are bounded continuous and integer, as well as categorical variables with explicitly enumerated domains. Extensive experimental results show the effectiveness of the new local search solver for these types of problems.
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Notes
- 1.
Note that the latter easily allows maximization as well, simply by having the function return the negative of the actual objective value.
- 2.
We note that we are unable to tune LocalSolver’s parameters with GGA due to LocalSolver’s license restrictions, meaning our results should only be seen as a lower bound on performance.
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Acknowledgements
The authors would like to thank the Paderborn Center for Parallel Computation (PC\(^2\)) for the use of the OCuLUS cluster.
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Sellmann, M., Tierney, K. (2020). Hyper-parameterized Dialectic Search for Non-linear Box-Constrained Optimization with Heterogenous Variable Types. In: Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2020. Lecture Notes in Computer Science(), vol 12096. Springer, Cham. https://doi.org/10.1007/978-3-030-53552-0_12
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