Using Genetic Programming for an Advanced Performance Assessment of Industrially Relevant Heterogeneous Catalysts

Response surface methodology using Gaussian processes: Towards optimizing the trans-stilbene epoxidation over Co2+–NaX catalysts

“…Currently, we are investigating improved formulations of the objective function for optimization, and the extension of the methodology to simultaneously optimize multiple objectives functions [52,53], or even objective function which is a time trajectory itself (e.g. conversion curve) [54]. Furthermore, in real industrial applications, the process factors and catalysts may not be as closely controlled as in the laboratories, and thus process variability may become significant.…”

Inorganic molecular sieves: Preparation, modification and industrial application in catalytic processes

“…HT experimentation combines different elements such as the automated parallel synthesis of solids, the parallel physico-chemical characterization, fast sequential testing of some of their most interesting properties, and the use of data mining techniques to maximize the information acquired [50][51][52][53][54].…”

A Matlab Genetic Programming Approach to Topographic Mesh Surface Generation

“…The generation of mathematical function by means of symbolic regression has been widely studied in the GP field, as shown by the following papers by Keijzer (2003), Streeter and Becker (2001), Iba and Nikolaev (2001), Parasuraman et al (2007), Miller and Harding (2008), Baumes et al (2009), Barmpalexis et al (2011), among others. It seems that the representation of surface plays an important role in a variety of disciplines including aided-design, computer vision, graphic computation and geographical signal and image processing.…”