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Abstract-Genetic programming is an evolutionary algorithm, which allows performing symbolic regression -the important task of obtaining the analytical form of a model by the data, produced by the model. One of the known problems of genetic programming is expressions' bloating that results in ineffictevely long expressions. To prevent bloating, symbolic simplification of expression is used. We introduce a new approach to simplification in genetic programming, making it a uniform part of the evolutionary process. To do that, we develop a genetic programming on the basis of transofmation rules, similarly to computer algebra systems. We compare our approach with existed solution, and prove its adequacy and effectiviness.

Genetic programming is a methodology, widely used in data mining for obtaining the analytic form that describes a given experimental data set. In some cases, genetic programming is complemented by symbolic computations that simplify found expressions. We propose to unify the induction of genetic programming with the deduction of symbolic computations in one genetic algorithm. Our approach was implemented as the .NET library and successfully tested at various data mining problems: function approximation, invariants finding and classification.

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We describe a metadata driven approach to development of stereotyped business accounting software. We modify the model-view-controller pattern by placing all the application's logic into a model, and automatically building controllers and views. The work has two essential parts. The first one is developing a way to define metadata, that can be altered in runtime, can depend on context and can store actions as well as data. The second is designing of the software as a data server, which stores a model and its metadata, and modify it by requests from various clients, e.g. web pages or windows applications. This approach was implemented in the Thornado framework and was used for creation of various applications. We justify the easiness and elegance of our implementation of the metadata driven development, and discuss advantages of the approach, such as cross-platformness, scalability and testability.