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. Alfaro-Fernández, Pedro; Ruiz, Rubén; Pagnozzi, Federico; Stützle, Thomas: Automatic algorithm design for hybrid flowshop scheduling problems (2020)
  2. Bowly, Simon; Smith-Miles, Kate; Baatar, Davaatseren; Mittelmann, Hans: Generation techniques for linear programming instances with controllable properties (2020)
  3. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  4. Fonseca, Gabriela B.; Nogueira, Thiago H.; Gómez Ravetti, Martín: A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem (2019)
  5. Franzin, Alberto; Stützle, Thomas: Revisiting simulated annealing: a component-based analysis (2019)
  6. Lu, Yongliang; Benlic, Una; Wu, Qinghua: A population algorithm based on randomized tabu thresholding for the multi-commodity pickup-and-delivery traveling salesman problem (2019)
  7. Lu, Yongliang; Hao, Jin-Kao; Wu, Qinghua: Hybrid evolutionary search for the traveling repairman problem with profits (2019)
  8. Perumal, Shyam S. G.; Larsen, Jesper; Lusby, Richard M.; Riis, Morten; Sørensen, Kasper S.: A matheuristic for the driver scheduling problem with staff cars (2019)
  9. Zhou, Qing; Benlic, Una; Wu, Qinghua; Hao, Jin-Kao: Heuristic search to the capacitated clustering problem (2019)
  10. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  11. Dellino, G.; Laudadio, T.; Mari, R.; Mastronardi, N.; Meloni, C.: Microforecasting methods for fresh food supply chain management: a computational study (2018)
  12. Dunning, Iain; Gupta, Swati; Silberholz, John: What works best when? A systematic evaluation of heuristics for max-cut and QUBO (2018)
  13. El Yafrani, Mohamed; Ahiod, Belaïd: Efficiently solving the traveling thief problem using hill climbing and simulated annealing (2018)
  14. Kanatchikov, Igor V.: Schrödinger wave functional in quantum Yang-Mills theory from precanonical quantization (2018)
  15. Li, Xiangyong; Zhu, Lanjian; Baki, Fazle; Chaouch, A. B.: Tabu search and iterated local search for the cyclic bottleneck assignment problem (2018)
  16. Lu, Yongliang; Benlic, Una; Wu, Qinghua: Multi-restart iterative search for the pickup and delivery traveling salesman problem with FIFO loading (2018)
  17. Lu, Yongliang; Benlic, Una; Wu, Qinghua: A memetic algorithm for the orienteering problem with mandatory visits and exclusionary constraints (2018)
  18. Myrvold, Wendy; Woodcock, Jennifer: A large set of torus obstructions and how they were discovered (2018)
  19. Andrade, Carlos E.; Ahmed, Shabbir; Nemhauser, George L.; Shao, Yufen: A hybrid primal heuristic for finding feasible solutions to mixed integer programs (2017)
  20. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)

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