JCLEC: a Java framework for evolutionary computation. In this paper we describe JCLEC, a Java software system for the development of evolutionary computation applications. This system has been designed as a framework, applying design patterns to maximize its reusability and adaptability to new paradigms with a minimum of programming effort. JCLEC architecture comprises three main modules: the core contains all abstract type definitions and their implementation; experiments runner is a scripting environment to run algorithms in batch mode; finally, GenLab is a graphical user interface that allows users to configure an algorithm, to execute it interactively and to visualize the results obtained. The use of JCLEC system is illustrated though the analysis of one case study: the resolution of the 0/1 knapsack problem by means of evolutionary algorithms.

References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Cano, Alberto; Luna, José María; Zafra, Amelia; Ventura, Sebastián: A classification module for genetic programming algorithms in JCLEC (2015)
  2. Xie, Xiao-Feng; Liu, Jiming; Wang, Zun-Jing: A cooperative group optimization system (2014)
  3. Cano, Alberto; Zafra, Amelia; Ventura, Sebastián: An interpretable classification rule mining algorithm (2013)
  4. García-Sánchez, P.; González, J.; Castillo, P.A.; Arenas, M.G.; Merelo-Guervós, J.J.: Service oriented evolutionary algorithms (2013)
  5. Cano, Alberto; Zafra, Amelia; Ventura, Sebastián: Speeding up the evaluation phase of GP classification algorithms on GPUs (2012)
  6. Gutiérrez, Pedro Antonio; Hervás-Martínez, César; Martínez-Estudillo, Francisco José; Carbonero, Mariano: A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems (2012)
  7. Olmo, J.L.; Romero, J.R.; Ventura, S.: Classification rule mining using ant programming guided by grammar with multiple Pareto fronts (2012)
  8. Parejo, José Antonio; Ruiz-Cortés, Antonio; Lozano, Sebastián; Fernandez, Pablo: Metaheuristic optimization frameworks: a survey and benchmarking (2012)
  9. Zafra, Amelia; Ventura, Sebastián: Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems (2012)
  10. García-Martínez, Carlos; Lozano, Manuel: Evaluating a local genetic algorithm as context-independent local search operator for metaheuristics (2010)
  11. Gutiérrez, Pedro Antonio; Hervás, César; Lozano, Manuel: Designing multilayer perceptrons using a guided saw-tooth evolutionary programming algorithm (2010)
  12. Merelo Guervós, Juan Julián; Castillo, Pedro A.; Alba, Enrique: Algorithm::Evolutionary, a flexible Perl module for evolutionary computation (2010)
  13. Zafra, Amelia; Ventura, Sebastián: G3P-MI: A genetic programming algorithm for multiple instance learning (2010)
  14. 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)
  15. Sağ, Tahir; çunkaş, Mehmet: A tool for multiobjective evolutionary algorithms (2009)