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. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)
  2. 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
  3. Cohen, D.; Crampton, J.; Gagarin, A.; Gutin, G.; Jones, M.: Algorithms for the workflow satisfiability problem engineered for counting constraints (2016)
  4. Di Gaspero, Luca; Rendl, Andrea; Urli, Tommaso: Balancing bike sharing systems with constraint programming (2016)
  5. Kilby, Philip; Urli, Tommaso: Fleet design optimisation from historical data using constraint programming and large neighbourhood search (2016)
  6. Boland, Natashia; Savelsbergh, Martin; Waterer, Hamish: A decision support tool for generating shipping data for the Hunter Valley coal chain (2015)
  7. Drexl, Michael; Schneider, Michael: A survey of variants and extensions of the location-routing problem (2015)
  8. Kattan, Ahmed; Fatima, Shaheen; Arif, Muhammad: Time-series event-based prediction: an unsupervised learning framework based on genetic programming (2015)
  9. 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)
  10. Reilly, Charles H.; Sapkota, Nabin: A family of composite discrete bivariate distributions with uniform marginals for simulating realistic and challenging optimization-problem instances (2015)
  11. Bartz-Beielstein, Thomas; Preuss, Mike: Experimental analysis of optimization algorithms: tuning and beyond (2014) ioport
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  15. Liao, Tianjun; Stützle, Thomas; Montes de Oca, Marco A.; Dorigo, Marco: A unified ant colony optimization algorithm for continuous optimization (2014)
  16. López-Ibáñez, Manuel; Stützle, Thomas: Automatically improving the anytime behaviour of optimisation algorithms (2014)
  17. Mascia, Franco; López-Ibáñez, Manuel; Dubois-Lacoste, Jérémie; Stützle, Thomas: Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools (2014)
  18. Naderi, Bahman; Ruiz, Rubén: A scatter search algorithm for the distributed permutation flowshop scheduling problem (2014)
  19. Yaghini, Masoud; Sarmadi, Mohammadreza; Nikoo, Nariman; Momeni, Mohsen: Capacity consumption analysis using heuristic solution method for under construction railway routes (2014)
  20. Kaucic, Massimiliano: A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization (2013)

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