Approximation capabilities of multilayer feedforward networks
Neural Networks
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Estimates of Approximation Rates by Gaussian Radial-Basis Functions
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Comparison of approaches for estimating reliability of individual regression predictions
Data & Knowledge Engineering
Combinatorial Development of Solid Catalytic Materials: Design of High-Throughput Experiments, Data Analysis, Data Mining
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary optimization of catalysts assisted by neural-network learning
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Surrogate modeling in the evolutionary optimization of catalytic materials
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The paper deals with surrogate modelling, a modern approach to the optimization of objective functions evaluated via measurements. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that is particularly attractive in evolutionary optimization. The paper recalls common strategies for using surrogate models in evolutionary optimization, and proposes two extensions to those strategies - extension to boosted surrogate models and extension to using a set of models. These are currently being implemented, in connection with surrogate modelling based on feed-forward neural networks, in a software tool for problem-tailored evolutionary optimization of catalytic materials. The paper presents results of experimentally testing already implemented parts and comparing boosted surrogate models with models without boosting, which clearly confirms the usefulness of both proposed extensions.