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. Andrade, Carlos E.; Toso, Rodrigo F.; Gonçalves, José F.; Resende, Mauricio G. C.: The multi-parent biased random-key genetic algorithm with implicit path-relinking and its real-world applications (2021)
  2. Anghinolfi, Davide; Paolucci, Massimo; Ronco, Roberto: A bi-objective heuristic approach for green identical parallel machine scheduling (2021)
  3. Diaz, Juan Esteban; López-Ibáñez, Manuel: Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms (2021)
  4. Jesus, Alexandre D.; Paquete, Luís; Liefooghe, Arnaud: A model of anytime algorithm performance for bi-objective optimization (2021)
  5. Smith-Miles, Kate; Christiansen, Jeffrey; Muñoz, Mario Andrés: Revisiting \textitwhereare the hard knapsack problems? Via instance space analysis (2021)
  6. Alfaro-Fernández, Pedro; Ruiz, Rubén; Pagnozzi, Federico; Stützle, Thomas: Automatic algorithm design for hybrid flowshop scheduling problems (2020)
  7. Bowly, Simon; Smith-Miles, Kate; Baatar, Davaatseren; Mittelmann, Hans: Generation techniques for linear programming instances with controllable properties (2020)
  8. Chagas, Jonatas B. C.; Wagner, Markus: Ants can orienteer a thief in their robbery (2020)
  9. Lanza-Gutierrez, Jose M.; Caballe, N. C.; Crawford, Broderick; Soto, Ricardo; Gomez-Pulido, Juan A.; Paredes, Fernando: Exploring further advantages in an alternative formulation for the set covering problem (2020)
  10. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  11. Fonseca, Gabriela B.; Nogueira, Thiago H.; Gómez Ravetti, Martín: A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem (2019)
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  13. 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)
  14. Lu, Yongliang; Hao, Jin-Kao; Wu, Qinghua: Hybrid evolutionary search for the traveling repairman problem with profits (2019)
  15. 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)
  16. Zhou, Qing; Benlic, Una; Wu, Qinghua; Hao, Jin-Kao: Heuristic search to the capacitated clustering problem (2019)
  17. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  18. Dellino, G.; Laudadio, T.; Mari, R.; Mastronardi, N.; Meloni, C.: Microforecasting methods for fresh food supply chain management: a computational study (2018)
  19. Dunning, Iain; Gupta, Swati; Silberholz, John: What works best when? A systematic evaluation of heuristics for max-cut and QUBO (2018)
  20. El Yafrani, Mohamed; Ahiod, Belaïd: Efficiently solving the traveling thief problem using hill climbing and simulated annealing (2018)

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