JCell is a framework for working mainly with cellular genetic algorithms (cGAs), but also it has implemented steady-state GAs, generational GAs, and distributed GAs (only a sequential version with ssGAs in the islands). The design of JCell allows the user to implement any metaheuristic, so we would be glad if you provide us your new algorithms (as well as operators, representations, etc.) implemented in JCell to add them to this repository. JCell is coded in Java and provides to the user some of the most recent proposed techniques in the literature of cGAs (e.g., self-adaptive and hierarchical populations, anysotropic selection, a novel multi-objective cGA, etc.). The use of JCell is very simple, since it is only required the manipulation of a simple configuration file (some example configuration files are provided in the cfg directory). JCell allows the user to work in combinatorial optimization, integer programming, continuous optimization, and multi-objective environments, all this with or without constraints. We consider JCell a really interesting and useful tool for future research, since it allows the combination of several new promising techniques recently published in the literature. Additionally, its careful design following the software engineering recommendations provides an intuitive code, allowing the user to easily make modifications and/or add new features to the framework. JCell is implemented in Java, a very well-known programming language that allows to execute our code in most computer platforms without any modification. It is possible because Java is an interpreted language, and thus a Java program can run in any kind computer having the Java Virtual Machine --the Java interpreter-- installed. JCell is compatible from version JDK 1.5 ahead

References in zbMATH (referenced in 21 articles )

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  1. Lu, Qiang; Zhou, Shuo; Tao, Fan; Luo, Jake; Wang, Zhiguang: Enhancing gene expression programming based on space partition and jump for symbolic regression (2021)
  2. Santiago, Alejandro; Dorronsoro, Bernabé; Nebro, Antonio J.; Durillo, Juan J.; Castillo, Oscar; Fraire, Héctor J.: A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME (2019)
  3. Leite, Nuno; Fernandes, Carlos M.; Melício, Fernando; Rosa, Agostinho C.: A cellular memetic algorithm for the examination timetabling problem (2018)
  4. Papoutsis-Kiachagias, E. M.; Giannakoglou, K. C.: Continuous adjoint methods for turbulent flows, applied to shape and topology optimization: industrial applications (2016)
  5. Nogueras, Rafael; Cotta, Carlos: A study on meme propagation in multimemetic algorithms (2015)
  6. Terán-Villanueva, J. David; Fraire Huacuja, Héctor Joaquín; Carpio Valadez, Juan Martín; Pazos Rangel, Rodolfo; Puga Soberanes, Héctor José; Martínez Flores, José A.: A heterogeneous cellular processing algorithm for minimizing the power consumption in wireless communications systems (2015)
  7. Apolloni, Javier; García-Nieto, José; Alba, Enrique; Leguizamón, Guillermo: Empirical evaluation of distributed differential evolution on standard benchmarks (2014)
  8. Vidal, Pablo; Luna, Francisco; Alba, Enrique: Systolic neighborhood search on graphics processing units (2014) ioport
  9. Al-Naqi, Asmaa; Erdogan, Ahmet T.; Arslan, Tughrul: Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes (2013) ioport
  10. Burguillo, Juan C.: Playing with complexity: from cellular evolutionary algorithms with coalitions to self-organizing maps (2013)
  11. Domínguez, Julián; Alba, Enrique: Dealing with hardware heterogeneity: a new parallel search model (2013) ioport
  12. Dorronsoro, Bernabé; Danoy, Grégoire; Nebro, Antonio J.; Bouvry, Pascal: Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution (2013)
  13. Nesmachnow, Sergio: Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing and grid systems (2013)
  14. Payne, Joshua L.; Giacobini, Mario; Moore, Jason H.: Complex and dynamic population structures: synthesis, open questions, and future directions (2013) ioport
  15. Guimarans, Daniel; Herrero, Rosa; Riera, Daniel; Juan, Angel A.; Ramos, Juan José: Combining probabilistic algorithms, constraint programming and Lagrangian relaxation to solve the vehicle routing problem (2011)
  16. Ishibuchi, Hisao; Sakane, Yuji; Tsukamoto, Noritaka; Nojima, Yusuke: Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization (2011) ioport
  17. Merelo Guervós, Juan Julián; Castillo, Pedro A.; Alba, Enrique: Algorithm::Evolutionary, a flexible Perl module for evolutionary computation (2010) ioport
  18. Munawar, Asim; Wahib, Mohamed; Munetomo, Masaharu; Akama, Kiyoshi: Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nvidia CUDA framework (2009) ioport
  19. Robles, I.; Alcalá, R.; Benítez, J. M.; Herrera, F.: Evolutionary parallel and gradually distributed lateral tuning of fuzzy rule-based systems (2009) ioport
  20. Alba, Enrique; Dorronsoro, Bernabé: Cellular genetic algorithms (2008)

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