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This paper presents how buy and sell trading rules are generated using gene expression programming with special setup. Market concepts are presented and market analysis is discussed with emphasis on technical analysis and quantitative methods. The use of genetic algorithms in deriving trading rules is presented. Gene expression programming is applied in a form where multiple types of operators and operands are used. This gives birth to multiple gene contexts and references between genes in order to keep the linear structure of the gene expression programming chromosome. The setup of multiple gene contexts is presented. The case study shows how to use the proposed gene setup to derive trading rules encoded by Boolean expressions, using a dataset with the reference exchange rates between the Euro and the Romanian leu. The conclusions highlight the positive results obtained in deriving useful trading rules.

In this paper, a method for model structure refinement is proposed and applied in estimation of cumulative number of vulnerabilities according to time. Security as a quality characteristic is presented and defined. Vulnerabilities are defined and their importance is assessed. Existing models used for number of vulnerabilities estimation are enumerated, inspecting their structure. The principles of genetic model generators are inspected. Model structure refinement is defined in comparison with model refinement and a method for model structure refinement is proposed. A case study shows how the method is applied and the obtained results.