Journal of Global Optimization
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Boosted Neural Networks in Evolutionary Computation
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
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
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
Case study: constraint handling in evolutionary optimization of catalytic materials
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Surrogate modeling in the evolutionary optimization of catalytic materials
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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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.