CIXL2: a crossover operator for evolutionary algorithms based on population features. We propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods.

References in zbMATH (referenced in 18 articles , 1 standard article )

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  1. Castelli, Mauro; Silva, Sara; Manzoni, Luca; Vanneschi, Leonardo: Geometric selective harmony search (2014)
  2. Chandra, Rohitash; Frean, Marcus; Zhang, Mengjie: Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks (2012)
  3. Tian, Jin; Li, Minqiang; Chen, Fuzan; Kou, Jisong: Coevolutionary learning of neural network ensemble for complex classification tasks (2012)
  4. Weise, Thomas; Chiong, Raymond; Tang, Ke: Evolutionary optimization: pitfalls and booby traps (2012)
  5. Kang, Fei; Li, Junjie; Ma, Zhenyue: Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions (2011)
  6. Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush: A novel bee swarm optimization algorithm for numerical function optimization (2010)
  7. García-Pedrajas, Nicolás: Supervised projection approach for boosting classifiers (2009)
  8. Karaboga, Dervis; Akay, Bahriye: A comparative study of artificial bee colony algorithm (2009)
  9. Li, Yaohang; Protopopescu, Vladimir A.; Arnold, Nikita; Zhang, Xinyu; Gorin, Andrey: Hybrid parallel tempering and simulated annealing method (2009)
  10. Dang, Jing; Brabazon, Anthony; O’Neill, Michael; Edelman, David: Estimation of an EGARCH volatility option pricing model using a bacteria foraging optimisation algorithm (2008)
  11. García-Martínez, C.; Lozano, M.; Herrera, F.; Molina, D.; Sánchez, A.M.: Global and local real-coded genetic algorithms based on parent-centric crossover operators (2008)
  12. García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo: Boosting random subspace method (2008)
  13. Ortiz-Boyer, Domingo; Hervás-Martínez, César; García-Pedrajas, Nicolás: Robust confidence intervals applied to crossover operator for real-coded genetic algorithms (2008)
  14. García-Pedrajas, Nicolás; García-Osorio, César; Fyfe, Colin: Nonlinear boosting projections for ensemble construction (2007)
  15. Karaboga, Dervis; Basturk, Bahriye: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm (2007)
  16. Ortiz-Boyer, Domingo; Hervás-Martínez, César; García-Pedrajas, Nicolás: Improving crossover operator for real-coded genetic algorithms using virtual parents (2007)
  17. Hervás-Martínez, C.; Ortiz-Boyer, D.: Analyzing the statistical features of CIXL2 crossover offspring (2005)
  18. Ortiz-Boyer, D.; Hervás-Martínez, C.; García-Pedrajas, N.: CIXL2: a crossover operator for evolutionary algorithms based on population features (2005)

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