Solving the problem of city ecology forecasting with neuro-evolutionary algorithms

In this paper the problem of a city ecological condition forecasting based on the chemical composition of the air is considered. The procedure of solving this problem with artificial neural networks, grown by evolutionary algorithms is described. Several modifications of evolutionary algorithms and ensemble approach for neural predictor design allowing the increase of prediction efficiency are presented. Also an ensemble method for neural predictor design is considered to increase the efficiency. The existing methods for design of intelligence information technologies ensembles have been considered. The comparison of their efficiency is presented in the paper for a set of test problems. A modified approach for artificial neural network ensembles is proposed, which is different from known before with combined application of existing schemes and methods for ensemble organization. In the problem description the problem of large amount of missing values in the dataset is highlighted. To solve this problem, a modified genetic programming method is applied. The usefulness of this method is shown for the problem solving. The testing shows the efficiency of the presented approach compared to basic and ensemble models. One of the applications of the developed algorithm is the time series prediction. Many technical systems contain a large amount of dynamic parameters, and tracking and predicting these parameters is an important problem. The rocket and space technology is no exception, so the described algorithm is a useful data analysis instrument for it. The developed approach can be used as a method for individual predictors’ creation, and also as way for combining the existing ones. It is shown that this approach allows increasing the accuracy of the resulting models.