Research And Application Of Gene Expression Programming In Heavy Metals Prediction Modeling

Heavy metal shape prediction model is to predict garbage form and migration of heavy metals in bottom ash into the core and key.Reasonable form prediction model of heavy metals, correctly evaluate urban house refuse the environmental impact of heavy metals in bottom ash, to explore the safety of the municipal solid waste incineration bottom ash disposal and reuse.The existing of municipal solid waste incineration fly ash in the prediction model has more than one hundred kinds of heavy metal form, but the model application is for general form of heavy metals in fly ash, ash and soil, etc., the form of heavy metals in waste incineration bottom ash prediction model has not yet.So study a kind of heavy metals in municipal solid waste incineration bottom ash shape prediction model is very important.GEP algorithm are introduced in detail in this paper, due to the standard algorithm have limitations, so do to algorithm improvement, this paper proposes a Jumping Gene Expression Programming algorithm(Jumping Gene Expression Programming, JM- GEP),;This article USES the Shannon entropy theory prove that JM- GEP convergence;, through the analysis of JM- GEP population under the martingale, theoretically proves the convergence of JM- GEP algorithm theory.Theory was applied to mor PHological changes of heavy metals, the time point of the mor PHology of heavy metal to predict the future trend.Heavy metals in this paper the data changes over time as sample, using the GEP and its improved algorithm study influence of heavy metals in the environment change trend, evolution prediction function is calculated, the heavy metals to establish the prediction model.The experimental simulation results show that the modeling method based on the two kinds of algorithm of heavy metals, can be either a specific form of heavy metals vary with time sequence modeling can also influence the environment of multivariable modeling, mor PHological changes of heavy metals, and the improved GEP algorithm can find more efficiently with heavy metals of the prediction model, so as to research in environmental governance has made significant progress.