Application of Crossover Operators Based on Confidence Interval in Modeling Problems Using Real-Coding Genetic Algorithms
نویسندگان
چکیده
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.
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