Regressor Survival Rate Estimation for Enhanced Crossover Configuration
In the framework of nonlinear systems identification by means of multiobjective genetic programming, the paper introduces a customized crossover operator, guided by fuzzy controlled regressor encapsulation. The approach is aimed at achieving a balance between exploration and exploitation by protecting well adapted subtrees from division during recombination. To reveal the benefits of the suggested genetic operator, the authors introduce a novel mathematical formalism which extends the Schema Theory for cut point crossover operating on trees encoding regressor based models. This general framework is afterwards used for monitoring the survival rates of fit encapsulated structural blocks. Other contributions are proposed in answer to the specific requirements of the identification problem, such as a customized tree building mechanism, enhanced elite processing and the hybridization with a local optimization procedure. The practical potential of the suggested algorithm is demonstrated in the context of an industrial application involving the identification of a subsection within the sugar factory of Lublin, Poland.
Keywordsgenetic operators fuzzy control schema theory nonlinear systems identification multiobjective optimization
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