**5. Conclusion**

By a new fabric developing, there is a need to know some relationships between the constructional parameters of fabrics and their predetermined end-usage properties in order to produce fabrics with desired quality. Fabric constructors develop a new fabric construction on the basis of their experiences or predictive models using different modelling tools of which deterministic and nondeterministic are distinguished. In general, the models obtained by deterministic modelling tools are the results of strict mathematical rules while in the case of models obtained by nondeterministic modelling tools, there are no precise, strict mathematical rules. Our study focused on the development of predictive models based on the genetic methods, e.g. genetic programming and genetic algorithms, in order to predict some porosity parameters of woven and nonwoven fabrics. Predictive models of the: 1. area of macro-pore cross-section and macro-pore density of woven fabrics based on the constructional parameters of woven fabrics (yarn linear density, weave factor, fabric tightness, denting), image analysis as testing method of porosity measurements, and genetic programming, and 2. total porosity and mean pore diameter of nonwoven fabrics based on the constructional parameters of nonwoven fabrics (fibre linear density, fabric mass per unit area, fabric thickness), mercury intrusion porosimetry as testing method of porosity measurements, and genetic algorithm, were developed. Open porosity and equivalent pore diameter of woven fabric were also predicted using values calculated on the basis of predictive models of the area of macro-pore cross-section and pore density, and known mathematical relationships. All proposed predictive models were created very precisely and could serve as guidelines for woven/nonwoven engineering in order to develop fabrics with the desired porosity parameters.

In general, for prediction of porosity parameters of woven or nonwoven samples both modelling tools can be used, e.g. GA and GP. Usually, GP method is used for more difficult problems. Our purpose was to show usability and effectiveness of both methods. By woven fabric modelling, the range of porosity parameters' measurements was substantial larger with more input variables when compared to the nonwoven fabrics (and this means more difficult problem), so the GP was used as modelling tool. By GP modelling, the models are developed in their symbolic forms, thus more precise models are developed in regard to the GA modelling, where only coefficients of prespecified models are defined. At the same time, for GP modelling more measurements data are desired for better model accuracy, while by GA modelling good results are achieved by lower number of measurements (in our case 27 measurements were available for woven fabrics and only 15 for nonwoven fabrics). The advantage of GP modelling is its excellent prediction accuracy, while its disadvantage is the complexity of the developed models. In general, by GA modelling, the developed models are simple but less accurate.
