SPOT: Sequential Parameter Optimization , R-Package for Sequential Parameter Optimization Toolbox. The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of di erent metamodeling approaches. This article exempli es how spot can be used for automatic and interactive tuning.

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  1. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  2. Andrade, Carlos E.; Ahmed, Shabbir; Nemhauser, George L.; Shao, Yufen: A hybrid primal heuristic for finding feasible solutions to mixed integer programs (2017)
  3. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)
  4. Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, Michel Lang: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions (2017) arXiv
  5. Redondo, J.L.; Fernández, J.; Ortigosa, P.M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  6. Schneider, Michael; Drexl, Michael: A survey of the standard location-routing problem (2017)
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  8. Di Gaspero, Luca; Rendl, Andrea; Urli, Tommaso: Balancing bike sharing systems with constraint programming (2016)
  9. Kilby, Philip; Urli, Tommaso: Fleet design optimisation from historical data using constraint programming and large neighbourhood search (2016)
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  15. Reilly, Charles H.; Sapkota, Nabin: A family of composite discrete bivariate distributions with uniform marginals for simulating realistic and challenging optimization-problem instances (2015)
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