GEATbx

GEATbx - The Genetic and Evolutionary Algorithm Toolbox for Matlab. The Genetic and Evolutionary Algorithm Toolbox provides global optimization capabilities in Matlab to solve problems not suitable for traditional optimization approaches. Are you looking for a sophisticated way of solving your problem in case it has no derivatives, is discontinuous, stochastic, non-linear or has multiple minima or maxima? The GEATbx should be your method of choice! Powerful genetic and evolutionary algorithms find solutions to your problems - and it’s easy to use! Numerous ready to run examples and demonstrations give you a head start in setting up your problem, selecting the appropriate optimization algorithm and monitoring the state and progress of the optimization. This enables beginners and advanced users to achieve results fast.


References in zbMATH (referenced in 14 articles )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Chowdhury, Souma; Tong, Weiyang; Messac, Achille; Zhang, Jie: A mixed-discrete particle swarm optimization algorithm with explicit diversity-preservation (2013)
  2. Talaslioglu, Tugrul: Global stability-based design optimization of truss structures using multiple objectives (2013)
  3. Mora-Gutiérrez, Roman Anselmo; Ramírez-Rodríguez, Javier; Rincón-García, Eric Alfredo; Ponsich, Antonin; Herrera, Oscar: An optimization algorithm inspired by social creativity systems (2012)
  4. Cervera, Joaquín; Baños, Alfonso: Nonlinear nonconvex optimization by evolutionary algorithms applied to robust control (2009)
  5. Sağ, Tahir; çunkaş, Mehmet: A tool for multiobjective evolutionary algorithms (2009)
  6. Voglis, C.; Lagaris, I.E.: Towards “Ideal multistart”. A stochastic approach for locating the minima of a continuous function inside a bounded domain (2009)
  7. Boschetti, Fabio: A local linear embedding module for evolutionary computation optimization (2008)
  8. Maaranen, Heikki; Miettinen, Kaisa; Penttinen, Antti: On initial populations of a genetic algorithm for continuous optimization problems (2007)
  9. Miettinen, Kaisa; Mäkelä, Marko M.; Maaranen, Heikki: Efficient hybrid methods for global continuous optimization based on simulated annealing (2006)
  10. Pasia, Joseph M.; Hermosilla, Augusto Y.; Ombao, Hernando: A useful tool for statistical estimation: genetic algorithms (2005)
  11. Kelner, V.; Léonard, O.: Application of genetic algorithms to lubrication pump stacking design (2004)
  12. Le Mauff, Frédéric; Duc, Gilles: Designing a low order robust controller for an active suspension system thank LMI, genetic algorithm and gradient search (2003)
  13. Digalakis, Jason G.; Margaritis, Konstantinos G.: An experimental study of benchmarking functions for genetic algorithms. (2002)
  14. Digalakis, J.G.; Margaritis, K.G.: On benchmarking functions for genetic algorithms (2001)