A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computational Optimization and Applications
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
ASAGA: an adaptive surrogate-assisted genetic algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Adaptive fuzzy fitness granulation for evolutionary optimization
International Journal of Approximate Reasoning
A memetic model of evolutionary PSO for computational finance applications
Expert Systems with Applications: An International Journal
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
Boosted Neural Networks in Evolutionary Computation
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
An evolutionary algorithm with spatially distributed surrogates for multiobjective optimization
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
An analysis of the equilibrium of migration models for biogeography-based optimization
Information Sciences: an International Journal
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
Surrogate model for continuous and discrete genetic optimization based on RBF networks
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Evolutionary optimization of catalysts assisted by neural-network learning
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A model-adaptive evolutionary algorithm for optimization
Artificial Life and Robotics
Resampling methods for meta-model validation with recommendations for evolutionary computation
Evolutionary Computation
Surrogate modeling in the evolutionary optimization of catalytic materials
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Coherent design methodology using modelling, simulation and optimisation
Proceedings of the 2011 Grand Challenges on Modeling and Simulation Conference
Optimizing cellular automata through a meta-model assisted memetic algorithm
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Application of variational granularity language sets in interactive genetic algorithms
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Automatic surrogate model type selection during the optimization of expensive black-box problems
Proceedings of the Winter Simulation Conference
Information Sciences: an International Journal
Evolution by adapting surrogates
Evolutionary Computation
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In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search. At the first level, the framework employs a data-parallel Gaussian process based global surrogate model to filter the evolutionary algorithm (EA) population of promising individuals. Subsequently, these potential individuals undergo a memetic search in the form of Lamarckian learning at the second level. The Lamarckian evolution involves a trust-region enabled gradient-based search strategy that employs radial basis function local surrogate models to accelerate convergence. Numerical results are presented on a series of benchmark test functions and on an aerodynamic shape design problem. The results obtained suggest that the proposed optimization framework converges to good designs on a limited computational budget. Furthermore, it is shown that the new algorithm gives significant savings in computational cost when compared to the traditional evolutionary algorithm and other surrogate assisted optimization frameworks