Abstract
In this work we develop and compare multi-parent crossover operators based on the extraction of characteristics from the best individuals in the population (average, median, standard deviation and quantiles). These statistics evolve in parallel with the algorithm. The proposed operators are used in combination with a real-coded genetic algorithm for the evolution of polynomial functions to solve microbial growth problems. Their performance is compared to other crossover operators for real-coded genetic algorithms. Both the prediction errors made in the modelling of systems and the objectivity and speed in the identification of models show the viability of this type of models that mix base functions with evolutionary computation.
This work has been funded by the spanish Ministry of Science and Technology, MCyT, through Project TIC 2002-04026-C02, and by FEDER funds.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baranyi, J., Roberts, T.A.: A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol. 23, 277–294 (1994)
Denison, D., Holmes, C., Mallick, B., Smith, A.: Bayesian Methods for Non-linear Classification and Regression. John Wiley & Sons, Chichester (2002)
Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms, vol. 21, pp. 187–202. Morgan Kaufmann, San Francisco (1993)
Hervás, C., Ortiz, B., García, N.: Theoretical Análisis of the Confidence Interval Based Crossover for Real-Coded Genetic Algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 153–161. Springer, Heidelberg (2002)
Myers Raymond, H.M., Montgomery, D.C.: Response Surface Methodology: Process and Product Optimisation Using Designed Experiments, 2nd edn. John Wiley & Sons, New York (2002)
Ortiz, D., Hervás, C., Muñoz, J.: Genetic algorithm with crossover based on confidence intervals as an alternative to least squares estimation for non-linear models. In: Metheuristic International Congress, Porto (2001)
Ortiz, D.: Operadores de cruce basado en intervalos de confianza en algoritmos genéticos con codificación real, Tesis Doctoral, Málaga (2001)
Rawlings, J.O., Pantula, S.G., Dickey, D.: Applied regression analysis: A research tool. Springer, New York (1998)
Rodríguez Pérez, R.: Elaboración de modelos predictivos de crecimiento microbiano de lactobacilus plantarum.Tesis Doctoral. Departamento de Bromatología y Tecnología de los alimentos. Universidad de Córdoba (2003)
Ventura, S., Ortíz, D., Hervás, C.: JCLEC. Una librería de clases Java para Computación Evolutiva. Congreso Español de Algoritmos Evolutivos y Bioinspirados (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
del Castillo Gomariz, R., Hervás Martínez, C., Ventura Soto, S., Ortiz Boyer, D. (2004). Application of Crossover Operators Based on Confidence Interval in Modeling Problems Using Real-Coding Genetic Algorithms. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-25945-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22218-7
Online ISBN: 978-3-540-25945-9
eBook Packages: Springer Book Archive