Genetic Expression Programming (GEP) is used to create Ground Motion Predicting Equations (GMPEs) for the average peak ground acceleration using 12,854 ground motion records obtained from the NGA-WEST2 project. The predictor set considered in this research consists of the moment magnitude, dip angle, rake angle, depth to the top of fault rupture, Joyner Boore distance, closest distance to the ruptured fault area, and the shear wave velocity in the top 30 m of the site. Four out of 23 candidate models were able to fairly predict the PGA for magnitudes larger than 4.5 and compared well with existing GMPEs in literature. GEP was capable of reasonably predicting the physical importance of the magnitude and distance parameters. However, other parameters often were either not fitted, or fitted as regression coefficients. The results illustrate GEP’s potential as a viable alternative to regression methods currently used in developing GMPEs.

<p>FIR filters are widely used nowadays, with applications from MP3 players, Hi-Fi systems, digital TVs, etc. to communication systems like wireless communication. They are implemented in DSPs and there are several trade-offs that make important to have an exact as possible estimation of the required filter order. </p><p>In order to find a better estimation of the filter order than the existing ones, genetic expression programming (GEP) is used. GEP is a Genetic Algorithm that can be used in function finding. It is implemented in a commercial application which, after the appropriate input file and settings have been provided, performs the evolution of the individuals in the input file so that a good solution is found. The thesis is the first one in this new research line. </p><p>The aim has been not only reaching the desired estimation but also pave the way for further investigations.</p>

FIR filters are widely used nowadays, with applications from MP3 players, Hi-Fi systems, digital TVs, etc. to communication systems like wireless communication. They are implemented in DSPs and there are several trade-offs that make important to have an exact as possible estimation of the required filter order. In order to find a better estimation of the filter order than the existing ones, genetic expression programming (GEP) is used. GEP is a Genetic Algorithm that can be used in function finding. It is implemented in a commercial application which, after the appropriate input file and settings have been provided, performs the evolution of the individuals in the input file so that a good solution is found. The thesis is the first one in this new research line. The aim has been not only reaching the desired estimation but also pave the way for further investigations.