Genetic Algorithm and Direct Search Toolbox

The Genetic Algorithm and Direct Search Toolbox (GADS) extends the optimization capabilities in MATLAB® and the Optimization Toolbox with tools for using the genetic and direct search algorithms. You can use these algorithms for problems that are difficult to solve with traditional optimization techniques, including problems that are not well defined or are difficult to model mathematically. You can also use them when computation of the objective function is discontinuous, highly nonlinear, stochastic, or has unreliable or undefined derivatives. The Genetic Algorithm and Direct Search Toolbox complements other optimization methods to help you find good starting points. You can then use traditional optimization techniques to refine your solution. Toolbox functions, which can be accessed through a graphical user interface (GUI) or the MATLAB command line, are written in the open MATLAB language. This means that you can inspect the algorithms, modify the source code, and create your own custom functions.


References in zbMATH (referenced in 27 articles )

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  1. Szopos, Ervin; Neag, Marius; Saracut, Ioana; Popescu, Victor; Topa, Marina: Synthesis tool based on genetic algorithm for FIR filters with user-defined magnitude characteristics (2016)
  2. Xue, Dingyü; Chen, YangQuan: Scientific computing with MATLAB (2016)
  3. Denisova, L. A.; Meshcheryakov, V. A.: Automatic parametric synthesis of a control system using the genetic algorithm (2015)
  4. Yu, Yue; Xu, Dinghua: On the inverse problem of thermal conductivity determination in nonlinear heat and moisture transfer model within textiles (2015)
  5. Comis Da Ronco, Claudio; Ponza, Rita; Benini, Ernesto: Aerodynamic shape optimization in aeronautics: a fast and effective multi-objective approach (2014)
  6. Ortiz, A.; Gorriz, J. M.; Ramirez, J.; Salas-Gonzalez, D.: Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering (2014) ioport
  7. Li, Haitao; Jiang, Dali: New model and heuristics for safety stock placement in general acyclic supply chain networks (2012)
  8. Sarasiri, Nuapett; Suthamno, Kittiwong; Sujitjorn, Sarawut: Bacterial foraging-tabu search metaheuristics for identification of nonlinear friction model (2012)
  9. Matallana, Luis G.; Blanco, Aníbal M.; Bandoni, J. Alberto: Nonlinear dynamic systems design based on the optimization of the domain of attraction (2011)
  10. Regis, Rommel G.: Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions (2011) ioport
  11. Cuevas-Tello, Juan C.; Tiňo, Peter; Raychaudhury, Somak; Yao, Xin; Harva, Markus: Uncovering delayed patterns in noisy and irregularly sampled time series: an astronomy application (2010)
  12. De Tommasi, Luciano; Gorissen, Dirk; Croon, Jeroen A.; Dhaene, Tom: Surrogate modeling of RF circuit blocks (2010)
  13. Di Domenico, Domenico; Fiengo, Giovanni; Stefanopoulou, Anna: A decoupled controller for fuel cell hybrid electric power split (2010)
  14. Perdahcıoğlu, D. Akçay; Ellenbroek, M. H. M.; Der Hoogt, P. J. M. Van; De Boer, A.: An optimization method for dynamics of structures with repetitive component patterns (2010) ioport
  15. Faris, W. F.; Ata, A. A.; Sa’adeh, M. Y.: Energy minimization approach for a two-link flexible manipulator (2009)
  16. Gosselin, Louis; Tye-Gingras, Maxime; Mathieu-Potvin, François: Review of utilization of genetic algorithms in heat transfer problems (2009)
  17. Gouveia, Paulo D. F.; Plakhov, Alexander; Torres, Delfim F. M.: Two-dimensional body of maximum mean resistance (2009)
  18. Gouveia, P. D. F.; Plakhov, A. Yu.; Torres, D. F. M.: On the two-dimensional rotational body of maximal Newtonian resistance (2009)
  19. Ndao, Sidy; Peles, Yoav; Jensen, Michael K.: Multi-objective thermal design optimization and comparative analysis of electronics cooling technologies (2009)
  20. Shiue, Yeou-Ren: Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach (2009)

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