GPRS-kit 0.2 User Guide

This toolkit provides a set of tools for automatically creating a response surface for a target design space. Response surfaces are equations that express a performance metric used to evaluate a set of possible designs in terms of the designs’ parameters. If you can parameterize your design and quantify a performance metric of interest to you then this toolkit can vastly reduce the number of highly accurate simulations or time-consuming experiments you must conduct in order to find globally optimal designs or important design space trends. By creating a predictive response surface from the measured performance of a small subset of designs, it is possible to evaluate all possible designs without taking the time to experimentally evaluate every single one. Numerical or analytic methods can be applied to the response surface in order to identify local or global optima, trends, and variable relationships. See [1, 3] for more information. Following in the footsteps of Alvarez [1], this toolkit uses genetic programming to create response surfaces from a suggested sample subset. Genetic programming works like a standard genetic algorithm, but operates on a population of non-linear polynomial equations (i.e. potential response surfaces) rather than a population of designs. While the sample used to create a GPRS can be randomly selected, it is often beneficial to use a Design of Experiments to guaranteed a representative sample. The Audze-Eglais Uniform Latin Hypercube Design of Experiments is particularly robust. This toolkit provides a reimplementation of the Audze-Eglais formulation algorithm described by Bates et al. [2]. This software is open sourced under the GPL license. I am not a professional software developer, and this software is far from perfect, but I want to make it available to anyone who might be interested in trying it out. Depending on interest expressed, new versions and improvements may appear.