**Author details**

196 Genetic Programming – New Approaches and Successful Applications

are depicted in Figure 13.

R² = 0,9024

72 74 76 78 80 82 84 86 88 90

**Total porosity (%)**

**Experimental values**

**5. Conclusion** 

70,0 72,0 74,0 76,0 78,0 80,0 82,0 84,0 86,0 88,0

**Predicted values**

more closed pores which are not detectable with mercury porosimetry.

In Figure 12, the theoretical values of total porosity and mean pore diameter as well as predicted values of pore diameter are linked with lines while samples (1-3, 4-7, 8-11, and 12- 15) are arranged regarding their decreased porosity. The results show that nonwovens with similar porous structure and lower porosity also have lower pore diameter. The experimental values of total porosity are for some samples not in a good agreement with theoretical ones, while samples which should have the highest porosity actually have the lowest (samples No. 1, 8, and 12). The reason may lie in fact, that these samples contain

The results show that the theoretical values of porosity parameters deviate from experimental ones on average by 8.0% (min 0.0%, max 15.4%) for total porosity and by 19.7% (min 2.9, max 57.3%) for pore diameter, whilst the predicted values, calculated using Equations 36-37, are in better agreement with the experimental ones. The mean predicted error is: 1.1% (from 0.0% to 4.4%) for the total porosity and 1.9% (from 0.0% to 12.4%) for the average pore diameter. The correlation coefficients between the predicted and experimental values are 0.9024 and 0.8492 for the total porosity and the average pore diameter, respectively. Scatter plots of the experimental and predicted values for porosity parameters,

R² = 0,8492

60 65 70 75 80 85 90 95

**Pore diameter (10-6 m)**

**Experimental values**

**Figure 13.** Scatter plots of experimental and predicted porosity parameters using GA models

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

60,0 65,0 70,0 75,0 80,0 85,0 90,0 95,0

**Predicted values**

Polona Dobnik Dubrovski *Department of Textile Materials and Design, University of Maribor, Faculty of Mechanical Engineering, Slovenia* 

Miran Brezočnik

*Department of Mechanical Engineering,University of Maribor, Faculty of Mechanical Engineering, Slovenia* 
