Abstract
This paper presents an important real-world application of both evolutionary computation and learning, an application to the search for optimal catalytic materials. In this area, evolutionary and especially genetic algorithms are encountered most frequently. However, their application is far from any standard methodology, due to problems with mixed optimization and constraints. The paper describes how these difficulties are dealt with in the evolutionary optimization system GENACAT, recently developed for searching optimal catalysts. It also recalls that the costly evaluation of objective functions in this application area can be tackled through learning suitable regression models of those functions, called surrogate models. Ongoing integration of neural-networks-based surrogate modelling with GENACAT is illustrated on two brief examples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baerns, M., Holeňa, M.: Combinatorial Development of Solid Catalytic Materials. Design of High-Throughput Experiments, Data Analysis, Data Mining. World Scientific, Singapore (2009)
Holeňa, M., Cukic, T., Rodemerck, U., Linke, D.: Optimization of catalysts using specific, description based genetic algorithms. Journal of Chemical Information and Modeling 48, 274–282 (2008)
Büche, D., Schraudolph, N., Koumoutsakos, P.: Accelerating evolutionary algorithms with gaussian process fitness function models. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 35, 183–194 (2005)
Jin, Y.: A comprehensive survery of fitness approximation in evolutionary computation. Soft Computing 9, 3–12 (2005)
Ulmer, H., Streichert, F., Zell, A.: Model assisted evolution strategies. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation, pp. 333–355. Springer, Heidelberg (2005)
Zhou, Z., Ong, Y., Nair, P., Keane, A., Lum, K.: Combining global and local surrogate models to accellerate evolutionary optimization. IEEE Transactions on Systems, Man and Cybernetics. Part C: Applications and Reviews 37, 66–76 (2007)
Baumes, L., Farrusseng, D., Lengliz, M., Mirodatos, C.: Using artificial neural networks to boost high-throughput discovery in heterogeneous catalysis. QSAR and Combinatorial Science 23, 767–778 (2004)
Farrusseng, D., Clerc, F., Mirodatos, C., Azam, N., Gilardoni, F., Thybaut, J., Balasubramaniam, P., Marin, G.: Development of an integrated informatics toolbox: HT kinetic and virtual screening. Combinatorial Chemistry and High Throughput Screening 10, 85–97 (2007)
Valero, S., Argente, E., Botti, V., Serra, J., Serna, P., Moliner, M., Corma, A.: DoE framework for catalyst development based on soft computing techniques. Computers and Chemical Engineering 33, 225–238 (2009)
Holeňa, M., Linke, D., Rodemerck, U., Bajer, L.: Neural networks as surrogate models for measurements in optimization algorithms. In: Al-Begain, K., Fiems, D., Knottenbelt, W. (eds.) Analytical and Stochastic Modeling Techniques and Applications. LNCS, vol. 6148, pp. 351–366. Springer, Heidelberg (2010)
Baumes, L., Blanché, A., Serna, P., Tchougang, A., Lachiche, N., Collet, P., Corma, A.: Using genetic programming for advanced performance assessment of industrially relevant heterogeneous catalysts. Materials and Manufacturing Processes 24, 282–292 (2009)
Wolf, D., Buyevskaya, O., Baerns, M.: An evolutionary approach in the combinatorial selection and optimization of catalytic materials. Applied Catalyst A: General 200, 63–77 (2000)
Ohrenberg, A., Törne, C., Schuppert, A., Knab, B.: Application of data mining and evolutionary optimization in catalyst discovery and high-throughput experimentation – techniques, strategies, and software. QSAR and Combinatorial Science 24, 29–37 (2005)
Pereira, R., Clerc, F., Farrusseng, D., Waal, J., Maschmeyer, T.: Effect of genetic algorithm parameters on the optimization of heterogeneous catalysts. QSAR and Combinatorial Science 24, 45–57 (2005)
Leary, S., Bhaskar, A., Keane, A.: A derivative based surrogate model for approximating and optimizing the output of an expensive computer simulation. Journal of Global Optimization 30, 39–58 (2004)
Ong, Y., Nair, P., Keane, A., Wong, K.: Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation, pp. 307–331. Springer, Berlin (2005)
Hosder, S., Watson, L., Grossman, B.: Polynomial response surface approximations for the multidisciplinary design optimization of a high speed civil transport. Optimization and Engineering 2, 431–452 (2001)
Holeňa, M., Linke, D., Steinfeldt, N.: Boosted neural networks in evolutionary computation. In: Chan, J.H. (ed.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 131–140. Springer, Heidelberg (2009)
Möhmel, S., Steinfeldt, N., Endgelschalt, S., Holeňa, M., Kolf, S., Dingerdissen, U., Wolf, D., Weber, R., Bewersdorf, M.: New catalytic materials for the high-temperature synthesis of hydrocyanic acid from methane and ammonia by high-throughput approach. Applied Catalysis A: General 334, 73–83 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Holeňa, M., Linke, D., Rodemerck, U. (2010). Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-17298-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17297-7
Online ISBN: 978-3-642-17298-4
eBook Packages: Computer ScienceComputer Science (R0)