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 55 articles )

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  1. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  2. Lu, Yongliang; Benlic, Una; Wu, Qinghua: A memetic algorithm for the orienteering problem with mandatory visits and exclusionary constraints (2018)
  3. Andrade, Carlos E.; Ahmed, Shabbir; Nemhauser, George L.; Shao, Yufen: A hybrid primal heuristic for finding feasible solutions to mixed integer programs (2017)
  4. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)
  5. 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
  6. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  7. Schneider, Michael; Drexl, Michael: A survey of the standard location-routing problem (2017)
  8. Cohen, D.; Crampton, J.; Gagarin, A.; Gutin, G.; Jones, M.: Algorithms for the workflow satisfiability problem engineered for counting constraints (2016)
  9. Di Gaspero, Luca; Rendl, Andrea; Urli, Tommaso: Balancing bike sharing systems with constraint programming (2016)
  10. Kilby, Philip; Urli, Tommaso: Fleet design optimisation from historical data using constraint programming and large neighbourhood search (2016)
  11. Momeni, Mohsen; Sarmadi, Mohammadreza: A genetic algorithm based on relaxation induced neighborhood search in a local branching framework for capacitated multicommodity network design (2016)
  12. Binois, Mickaël; Rullière, Didier; Roustant, Olivier: On the estimation of Pareto fronts from the point of view of copula theory (2015)
  13. Boland, Natashia; Savelsbergh, Martin; Waterer, Hamish: A decision support tool for generating shipping data for the Hunter Valley coal chain (2015)
  14. Drexl, Michael; Schneider, Michael: A survey of variants and extensions of the location-routing problem (2015)
  15. Kattan, Ahmed; Fatima, Shaheen; Arif, Muhammad: Time-series event-based prediction: an unsupervised learning framework based on genetic programming (2015)
  16. Mernik, Marjan; Liu, Shih-Hsi; Karaboga, Dervis; Črepinšek, Matej: On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation (2015)
  17. Reilly, Charles H.; Sapkota, Nabin: A family of composite discrete bivariate distributions with uniform marginals for simulating realistic and challenging optimization-problem instances (2015)
  18. Bartz-Beielstein, Thomas; Preuss, Mike: Experimental analysis of optimization algorithms: tuning and beyond (2014) ioport
  19. Caraffini, Fabio; Neri, Ferrante; Picinali, Lorenzo: An analysis on separability for memetic computing automatic design (2014) ioport
  20. Hutter, Frank; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: Algorithm runtime prediction: methods & evaluation (2014)

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