Library design using genetic algorithms for catalyst discovery and optimization
Résumé
This study reports a detailed investigation of catalyst library design by genetic algorithm (GA). A methodol. for assessing GA configurations is described. Operators, which promote the optimization speed while being robust to noise and outliers, are revealed through statistical studies. The genetic algorithms were implemented in GA platform software called OptiCat, which enables the construction of custom-made workflows using a tool box of operators. Two sep. studies were carried out (i) on a virtual benchmark and (ii) on real surface response which is derived from HT screening. Addnl., we report a methodol. to model a complex surface response by binning the search space in small zones that are then independently modeled by linear regression. In contrast to artificial neural networks, this approach allows one to obtain an explicit model in an analogical form that can be further used in Excel or entered in OptiCat to perform simulations. While speeding the implementation of a hybrid algorithm combining a GA with a knowledge-based extn. engine is described, while speeding up the optimization process by means of virtual prescreening the hybrid GA enables one to open the "black-box" by providing knowledge as a set of assocn. rules.