On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms

Gobin, OC; Schüth, F

HERO ID

875564

Reference Type

Journal Article

Year

2008

Language

English

PMID

18693763

HERO ID 875564
In Press No
Year 2008
Title On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms
Authors Gobin, OC; Schüth, F
Journal Journal of Combinatorial Chemistry
Volume 10
Issue 6
Page Numbers 835-846
Abstract Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.
Doi 10.1021/cc800046u
Pmid 18693763
Wosid WOS:000260851400007
Is Certified Translation No
Dupe Override No
Comments Source: Web of Science 000260851400007
Is Public Yes
Language Text English
Is Qa No