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. 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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