EO is a template-based, ANSI-C++ evolutionary computation library which helps you to write your own stochastic optimization algorithms insanely fast.Evolutionary algorithms forms a family of algorithms inspired by the theory of evolution, that solve various problems. They evolve a set of solutions to a given problem, in order to produce the best results. These are stochastic algorithms, because they iteratively use random processes. The vast majority of these methods are used to solve optimization problems, and may be also called ”metaheuristics”. They are also ranked among computational intelligence methods, a domain close to artificial intelligence. With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems, from continuous to combinatorial ones.

References in zbMATH (referenced in 19 articles , 1 standard article )

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  1. Giagkiozis, Ioannis; Purshouse, Robin C.; Fleming, Peter J.: An overview of population-based algorithms for multi-objective optimisation (2015)
  2. Humeau, J.; Liefooghe, A.; Talbi, E.-G.; Verel, S.: ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms (2013)
  3. Simons, C. L.; Smith, J. E.: A comparison of meta-heuristic search for interactive software design (2013) ioport
  4. Liefooghe, Arnaud; Jourdan, Laetitia; Talbi, El-Ghazali: A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO (2011) ioport
  5. Liefooghe, Arnaud; Jourdan, Laetitia; Legrand, Thomas; Humeau, Jérémie; Talbi, El-Ghazali: ParadisEO-MOEO: A software framework for evolutionary multi-objective optimization (2010)
  6. Merelo Guervós, Juan Julián; Castillo, Pedro A.; Alba, Enrique: Algorithm::Evolutionary, a flexible Perl module for evolutionary computation (2010) ioport
  7. Tantar, Alexandru-Adrian; Melab, Nouredine; Talbi, El-Ghazali: A grid-based hybrid hierarchical genetic algorithm for protein structure prediction (2010)
  8. Alcalá-Fdez, J.; Sánchez, L.; García, S.; del Jesus, M. J.; Ventura, S.; Garrell, J. M.; Otero, J.; Romero, C.; Bacardit, J.; Rivas, V. M.; Fernández, J. C.; Herrera, F.: KEEL: a software tool to assess evolutionary algorithms for data mining problems (2009) ioport
  9. da Motta Salles Barreto, André; Anderson, Charles W.: Restricted gradient-descent algorithm for value-function approximation in reinforcement learning (2008)
  10. Ventura, Sebastián; Romero, Cristóbal; Zafra, Amelia; Delgado, José A.; Hervás, César: JCLEC: a Java framework for evolutionary computation (2008) ioport
  11. Mattfeld, Dirk C.; Orth, Holger: The allocation of storage space for transshipment in vehicle distribution (2006)
  12. Melab, N.; Cahon, S.; Talbi, E-G.: Grid computing for parallel bioinspired algorithms (2006)
  13. Cahon, S.; Melab, N.; Talbi, E.-G.; Schoenauer, M.: ParaDisEO-based design of parallel and distributed evolutionary algorithms (2004)
  14. Collet, Pierre; Schoenauer, Marc: GUIDE: Unifying evolutionary engines through a graphical user interface (2004)
  15. Johnson, Clayton M.; Farrell, James: Evolutionary induction of grammar systems for multi-agent cooperation (2004)
  16. Rivas, V. M.; Merelo, J. J.; Castillo, P. A.; Arenas, M. G.; Castellano, J. G.: Evolving RBF neural networks for time-series forecasting with EvRBF (2004) ioport
  17. Godzik, Nicolas; Schoenauer, Marc; Sebag, Michèle: Evolving symbolic controllers (2003)
  18. Eggermont, Jeroen: Evolving fuzzy decision trees with genetic programming and clustering (2002)
  19. Keijzer, M.; Merelo, J. J.; Romero, G.; Schoenauer, Marc: Evolving objects: A general purpose evolutionary computation library (2002)