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)
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  3. Corazza, Marco; di Tollo, Giacomo; Fasano, Giovanni; Pesenti, Raffaele: A novel hybrid PSO-based metaheuristic for costly portfolio selection problems (2021)
  4. Diaz, Juan Esteban; López-Ibáñez, Manuel: Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms (2021)
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  7. Alfaro-Fernández, Pedro; Ruiz, Rubén; Pagnozzi, Federico; Stützle, Thomas: Automatic algorithm design for hybrid flowshop scheduling problems (2020)
  8. Bowly, Simon; Smith-Miles, Kate; Baatar, Davaatseren; Mittelmann, Hans: Generation techniques for linear programming instances with controllable properties (2020)
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  10. 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)
  11. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
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  14. 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)
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  16. 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)
  17. Zhou, Qing; Benlic, Una; Wu, Qinghua; Hao, Jin-Kao: Heuristic search to the capacitated clustering problem (2019)
  18. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  19. Dellino, G.; Laudadio, T.; Mari, R.; Mastronardi, N.; Meloni, C.: Microforecasting methods for fresh food supply chain management: a computational study (2018)
  20. Dunning, Iain; Gupta, Swati; Silberholz, John: What works best when? A systematic evaluation of heuristics for max-cut and QUBO (2018)

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