SPOT

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.


References in zbMATH (referenced in 86 articles )

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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)
  12. Franzin, Alberto; Stützle, Thomas: Revisiting simulated annealing: a component-based analysis (2019)
  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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