Machine Learning
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Radial Basis Functions
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Estimation of individual prediction reliability using the local sensitivity analysis
Applied Intelligence
Comparison of approaches for estimating reliability of individual regression predictions
Data & Knowledge Engineering
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
The Pareto-following variation operator as an alternative approximation model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
A mono surrogate for multiobjective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Combinatorial Development of Solid Catalytic Materials: Design of High-Throughput Experiments, Data Analysis, Data Mining
Neural networks as surrogate models for measurements in optimization algorithms
ASMTA'10 Proceedings of the 17th international conference on Analytical and stochastic modeling techniques and applications
Evolutionary optimization of catalysts assisted by neural-network learning
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Case study: constraint handling in evolutionary optimization of catalytic materials
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Regression conformal prediction with nearest neighbours
Journal of Artificial Intelligence Research
Speedups between ×70 and ×120 for a generic local search (memetic) algorithm on a single GPGPU chip
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Accelerating evolutionary algorithms with Gaussian process fitness function models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
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The search for best performing catalysts leads to high-dimensional optimization tasks. They are by far most frequently tackled using evolutionary algorithms, usually implemented in systems developed specifically for the area of catalysis. Their fitness functions are black-box functions with costly and time-consuming empirical evaluation. This suggests to apply surrogate modeling. The paper points out three difficulties challenging the application of surrogate modeling to catalysts optimization: mixed-variables optimization, assessing the suitability of different models, and scalarization of multiple objectives. It then provides examples of how those challenges are tackled in real-world catalysts optimization tasks. The examples are based on results obtained in three such tasks using one of the leading specific evolutionary optimization systems for catalysis.