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 63 articles )

Showing results 1 to 20 of 63.
Sorted by year (citations)

1 2 3 4 next

  1. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  2. Li, Xiangyong; Zhu, Lanjian; Baki, Fazle; Chaouch, A. B.: Tabu search and iterated local search for the cyclic bottleneck assignment problem (2018)
  3. Lu, Yongliang; Benlic, Una; Wu, Qinghua: Multi-restart iterative search for the pickup and delivery traveling salesman problem with FIFO loading (2018)
  4. Lu, Yongliang; Benlic, Una; Wu, Qinghua: A memetic algorithm for the orienteering problem with mandatory visits and exclusionary constraints (2018)
  5. Andrade, Carlos E.; Ahmed, Shabbir; Nemhauser, George L.; Shao, Yufen: A hybrid primal heuristic for finding feasible solutions to mixed integer programs (2017)
  6. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)
  7. 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
  8. Chen, Yuning; Hao, Jin-Kao: An iterated “hyperplane exploration” approach for the quadratic knapsack problem (2017)
  9. 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)
  10. Li, Xiangyong; Wei, Kai; Aneja, Y. P.; Tian, Peng; Cui, Youzhi: Matheuristics for the single-path design-balanced service network design problem (2017)
  11. Ma, Fuda; Hao, Jin-Kao; Wang, Yang: An effective iterated tabu search for the maximum bisection problem (2017)
  12. Pan, Quan-Ke; Ruiz, Rubén; Alfaro-Fernández, Pedro: Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows (2017)
  13. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  14. Schneider, Michael; Drexl, Michael: A survey of the standard location-routing problem (2017)
  15. Syrichas, A.; Crispin, A.: Large-scale vehicle routing problems: quantum annealing, tunings and results (2017)
  16. Cohen, D.; Crampton, J.; Gagarin, A.; Gutin, G.; Jones, M.: Algorithms for the workflow satisfiability problem engineered for counting constraints (2016)
  17. Di Gaspero, Luca; Rendl, Andrea; Urli, Tommaso: Balancing bike sharing systems with constraint programming (2016)
  18. Kilby, Philip; Urli, Tommaso: Fleet design optimisation from historical data using constraint programming and large neighbourhood search (2016)
  19. Momeni, Mohsen; Sarmadi, Mohammadreza: A genetic algorithm based on relaxation induced neighborhood search in a local branching framework for capacitated multicommodity network design (2016)
  20. Binois, Mickaël; Rullière, Didier; Roustant, Olivier: On the estimation of Pareto fronts from the point of view of copula theory (2015)

1 2 3 4 next