PGAPack is a general-purpose, data-structure-neutral parallel GENETIC ALGORITHM library. It is intended to provide most capabilities desired in a genetic algorithm library, in an integrated, seamless, and portable manner. Features include: Callable from Fortran or C. Runs on uniprocessors, parallel computers, and workstation networks. Binary-, integer-, and real- and character-valued native data types Full extensibility to support custom operators and new data types. Easy-to-use interface for novice and application users. Multiple levels of access for expert users. Extensive debugging facilities. Large set of example problems. Detailed users guide Parameterized POPULATION replacement. Multiple choices for SELECTION, CROSSOVER, and MUTATION operators Easy integration of hill-climbing heuristics.

References in zbMATH (referenced in 16 articles , 1 standard article )

Showing results 1 to 16 of 16.
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  1. Bożejko, Wojciech: A new class of parallel scheduling algorithms. (2010)
  2. Güngör-Demirci, Gamze; Aksoy, Ayşegül: Evaluation of the genetic algorithm parameters on the optimization performance: a case study on pump-and-treat remediation design (2010) ioport
  3. Whittaker, Gerald; Confesor, Remegio jun.; Griffith, Stephen M.; Färe, Rolf; Grosskopf, Shawna; Steiner, Jeffrey J.; Mueller-Warrant, George W.; Banowetz, Gary M.: A hybrid genetic algorithm for multiobjective problems with activity analysis-based local search (2009)
  4. Alba, Enrique; Chicano, Francisco: Observations in using parallel and sequential evolutionary algorithms for automatic software testing (2008)
  5. Vaz, A. Ismael F.; Vicente, Luís N.: A particle swarm pattern search method for bound constrained global optimization (2007)
  6. Kim, Kwiseon; Graf, Peter A.; Jones, Wesley B.: A genetic algorithm based inverse band structure method for semiconductor alloys (2005)
  7. Digalakis, J.; Margaritis, K.: Performance comparison of memetic algorithms (2004)
  8. Nazareth, John Lawrence: Differentiable optimization and equation solving. A treatise on algorithmic science and the Karmarkar revolution (2003)
  9. Alba, E.; Almeida, F.; Blesa, M.; Cabeza, J.; Cotta, C.; Díaz, M.; Dorta, I.; Gabarró, J.; León, C.; Luna, J.; Moreno, L.; Pablos, C.; Petit, J.; Rojas, A.; Xhafa, F.: MALLBA: A library of skeletons for combinatorial optimisation (2002)
  10. Digalakis, Jason G.; Margaritis, Konstantinos G.: An experimental study of benchmarking functions for genetic algorithms. (2002)
  11. Macías-Macías, Miguel; García-Orellana, Carlos J.; González-Velasco, Horacio M.; Gallardo-Caballero, Ramón; Serrano-Pérez, Antonio: A comparison of PCA and GA selected features for cloud field classification (2002)
  12. Digalakis, J. G.; Margaritis, K. G.: On benchmarking functions for genetic algorithms (2001)
  13. Nazareth, J. L.: Multialgorithms for parallel computing: A new paradigm for optimization (2001)
  14. Christou, Ioannis T.; Meyer, Robert R.: Decomposition algorithms for communication minimization in parallel computing. (2000)
  15. Booker, Andrew J.; Dennis, J. E. jun.; Frank, Paul D.; Serafini, David B.; Torczon, Virginia: Optimization using surrogate objectives on a helicopter test example (1998)
  16. Niemann, Ralf: Hardware/ software co-design for data flow dominated embedded systems (1998)