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 40 articles )

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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: A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems (2011)

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