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. Fonseca, Gabriela B.; Nogueira, Thiago H.; Gómez Ravetti, Martín: A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem (2019)
  2. 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)
  3. 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)
  4. Zhou, Qing; Benlic, Una; Wu, Qinghua; Hao, Jin-Kao: Heuristic search to the capacitated clustering problem (2019)
  5. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  6. Li, Xiangyong; Zhu, Lanjian; Baki, Fazle; Chaouch, A. B.: Tabu search and iterated local search for the cyclic bottleneck assignment problem (2018)
  7. Lu, Yongliang; Benlic, Una; Wu, Qinghua: Multi-restart iterative search for the pickup and delivery traveling salesman problem with FIFO loading (2018)
  8. Lu, Yongliang; Benlic, Una; Wu, Qinghua: A memetic algorithm for the orienteering problem with mandatory visits and exclusionary constraints (2018)
  9. Andrade, Carlos E.; Ahmed, Shabbir; Nemhauser, George L.; Shao, Yufen: A hybrid primal heuristic for finding feasible solutions to mixed integer programs (2017)
  10. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)
  11. 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
  12. Chen, Yuning; Hao, Jin-Kao: An iterated “hyperplane exploration” approach for the quadratic knapsack problem (2017)
  13. Diaz, Juan Esteban; Handl, Julia; Xu, Dong-Ling: Evolutionary robust optimization in production planning -- interactions between number of objectives, sample size and choice of robustness measure (2017)
  14. Li, Xiangyong; Wei, Kai; Aneja, Y. P.; Tian, Peng; Cui, Youzhi: Matheuristics for the single-path design-balanced service network design problem (2017)
  15. Ma, Fuda; Hao, Jin-Kao; Wang, Yang: An effective iterated tabu search for the maximum bisection problem (2017)
  16. Pan, Quan-Ke; Ruiz, Rubén; Alfaro-Fernández, Pedro: Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows (2017)
  17. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  18. Schneider, Michael; Drexl, Michael: A survey of the standard location-routing problem (2017)
  19. Syrichas, A.; Crispin, A.: Large-scale vehicle routing problems: quantum annealing, tunings and results (2017)
  20. Cohen, D.; Crampton, J.; Gagarin, A.; Gutin, G.; Jones, M.: Algorithms for the workflow satisfiability problem engineered for counting constraints (2016)

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