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. Di Gaspero, Luca; Rendl, Andrea; Urli, Tommaso: Balancing bike sharing systems with constraint programming (2016)
  2. Kilby, Philip; Urli, Tommaso: Fleet design optimisation from historical data using constraint programming and large neighbourhood search (2016)
  3. Boland, Natashia; Savelsbergh, Martin; Waterer, Hamish: A decision support tool for generating shipping data for the Hunter Valley coal chain (2015)
  4. Drexl, Michael; Schneider, Michael: A survey of variants and extensions of the location-routing problem (2015)
  5. Reilly, Charles H.; Sapkota, Nabin: A family of composite discrete bivariate distributions with uniform marginals for simulating realistic and challenging optimization-problem instances (2015)
  6. Bartz-Beielstein, Thomas; Preuss, Mike: Experimental analysis of optimization algorithms: tuning and beyond (2014)
  7. Caraffini, Fabio; Neri, Ferrante; Picinali, Lorenzo: An analysis on separability for memetic computing automatic design (2014)
  8. Hutter, Frank; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: Algorithm runtime prediction: methods & evaluation (2014)
  9. Lacroix, Benjamin; Molina, Daniel; Herrera, Francisco: Region based memetic algorithm for real-parameter optimisation (2014)
  10. Liao, Tianjun; Stützle, Thomas; Montes de Oca, Marco A.; Dorigo, Marco: A unified ant colony optimization algorithm for continuous optimization (2014)
  11. López-Ibáñez, Manuel; Stützle, Thomas: Automatically improving the anytime behaviour of optimisation algorithms (2014)
  12. Mascia, Franco; López-Ibáñez, Manuel; Dubois-Lacoste, Jérémie; Stützle, Thomas: Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools (2014)
  13. Naderi, Bahman; Ruiz, Rubén: A scatter search algorithm for the distributed permutation flowshop scheduling problem (2014)
  14. Yaghini, Masoud; Sarmadi, Mohammadreza; Nikoo, Nariman; Momeni, Mohsen: Capacity consumption analysis using heuristic solution method for under construction railway routes (2014)
  15. Kaucic, Massimiliano: A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization (2013)
  16. Liao, Tianjun; de Oca, Marco A.Montes; Stützle, Thomas: Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set (2013)
  17. Lopes, Leo; Smith-Miles, Kate: Generating applicable synthetic instances for branch problems (2013)
  18. Yaghini, Masoud; Momeni, Mohsen; Sarmadi, Mohammadreza; Ahadi, Hamid Reza: An efficient heuristic algorithm for the capacitated $p$-median problem (2013)
  19. Zaefferer, Martin; Bartz-Beielstein, Thomas; Naujoks, Boris; Wagner, Tobias; Emmerich, Michael: A case study on multi-criteria optimization of an event detection software under limited budgets (2013)
  20. Dubois-Lacoste, Jérémie; López-Ibáñez, Manuel; Stützle, Thomas: Improving the anytime behavior of two-phase local search (2011)

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