Symbolic Regression by Genetic Programming: Using the Prediction of PCDDs/PCDFs Emissions from Incinerators as an Example
【摘要】：正 Recent advances of genetic algorithm bave resulted in a dramatic growth of optimization and prediction techniques for Inonlinear models. The use of a tree-structured genetic algorithm for nonlinear model identification can be regraded as an evolutionary technique in system identification. Such a genetic programming approach is particularly adapted in this paper for the system identification of nonlinear structure with the unique condition of small scale samples. Example is drawn from the emission test of PCDDs/PCDFs through the flue gas discharge from several municipal incinerators in Canada. It shows genetic programming may successfully solves the representation problem of nonlinear models with higher flexibility while considering the inherent problem-oriented mecharnism existing in real world systems.