The Genetic Algorithm Toolbox for MATLAB ® was developed at the Department of Automatic Control and Systems Engineering of The University of Sheffield, UK, in order to make GA’s accessible to the control engineer within the framework of a existing computer-aided control system design package. The toolbox was written with the support of a UK SERC grant, and the final version (v1.2) was completed in 1994. The Toolbox was originally developed for MATLAB v4.2 but has also been successfully used with subsequent versions up to and including MATLAB 7. For a more detailed introduction to the capabilities and use of the GA Toolbox, please refer to the introductory papers and user’s guide detailed below and available for download opposite.

References in zbMATH (referenced in 60 articles )

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  1. Sala, Ramses; Baldanzini, Niccolò; Pierini, Marco: Global optimization test problems based on random field composition (2017)
  2. Tenne, Yoel: Machine-learning in optimization of expensive black-box functions (2017)
  3. Wang, Shijin; Liu, Ming: Two-machine flow shop scheduling integrated with preventive maintenance planning (2016)
  4. Tenne, Yoel: An adaptive-topology ensemble algorithm for engineering optimization problems (2015)
  5. Feng, Guodong; Liu, Min; Wang, Guoli: Genetic algorithm based optimal placement of PIR sensors for human motion localization (2014)
  6. Mu, Tingting; Miwa, Makoto; Tsujii, Junichi; Ananiadou, Sophia: Discovering robust embeddings in (dis)similarity space for high-dimensional linguistic features (2014)
  7. Duarte-Mermoud, M. A.; Beltrán, N. H.; Salah, S. A.: Probabilistic adaptive crossover applied to Chilean wine classification (2013) ioport
  8. Wu, Jui-Yu: Solving unconstrained global optimization problems via hybrid swarm intelligence approaches (2013)
  9. Angelova, Maria; Atanassov, Krassimir; Pencheva, Tania: Purposeful model parameters genesis in simple genetic algorithms (2012) ioport
  10. He, Wei; Bindel, David; Govindjee, Sanjay: Topology optimization in micromechanical resonator design (2012)
  11. Jiang, Mingfeng; Liu, Feng; Wang, Yaming; Shou, Guofa; Huang, Wenqing; Zhang, Huaxiong: A hybrid model of Maximum Margin Clustering method and Support Vector Regression for noninvasive electrocardiographic imaging (2012)
  12. Tenne, Yoel; Izui, Kazuhiro; Nishiwaki, Shinji: A hybrid model-classifier framework for managing prediction uncertainty in expensive optimisation problems (2012)
  13. Wu, Jui-Yu: Solving constrained global optimization problems by using hybrid evolutionary computing and artificial life approaches (2012)
  14. Parente, Paulo M. D. C.; Smith, Richard J.: GEL methods for nonsmooth moment indicators (2011)
  15. Salim, Reem; Bettayeb, Maamar: $H_2$ and $H_\infty$ optimal model reduction using genetic algorithms (2011)
  16. 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)
  17. Liu, Ruochen; Jiao, Licheng; Li, Yangyang; Liu, Jing: An immune memory clonal algorithm for numerical and combinatorial optimization (2010)
  18. Monje, Concepción Alicia; Chen, YangQuan; Vinagre, Blas Manuel; Xue, Dingyü; Feliu, Vicente: Fractional-order systems and controls. Fundamentals and applications (2010)
  19. Evsukoff, Alexandre G.; Galichet, Sylvie; De Lima, Beatriz S. L. P.; Ebecken, Nelson F. F.: Design of interpretable fuzzy rule-based classifiers using spectral analysis with structure and parameters optimization (2009)
  20. Gosselin, Louis; Tye-Gingras, Maxime; Mathieu-Potvin, François: Review of utilization of genetic algorithms in heat transfer problems (2009)

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