Foundations of genetic algorithms
Foundations of genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming and Evolvable Machines
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Evolving neural networks through augmenting topologies
Evolutionary Computation
Automated Discovery of Numerical Approximation Formulae via Genetic Programming
Genetic Programming and Evolvable Machines
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Parameter space exploration with Gaussian process trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Grid enabled sequential design and adaptive metamodeling
Proceedings of the 38th conference on Winter simulation
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Hierarchical Nonlinear Approximation for Experimental Design and Statistical Data Fitting
SIAM Journal on Scientific Computing
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Multidimensional sequential sampling for NURBs-based metamodel development
Engineering with Computers
Theoretical analysis of genetic algorithms in noisy environments based on a Markov Model
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Sequential modeling of a low noise amplifier with neural networks and active learning
Neural Computing and Applications
Particle Swarm Model Selection
The Journal of Machine Learning Research
IEEE Transactions on Evolutionary Computation
A comparative study of metamodeling methods for multiobjective crashworthiness optimization
Computers and Structures
Multiobjective global surrogate modeling, dealing with the 5-percent problem
Engineering with Computers
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Evolutionary regression modeling with active learning: an application to rainfall runoff modeling
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
A novel sequential design strategy for global surrogate modeling
Winter Simulation Conference
Planning multiple paths with evolutionary speciation
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Local function approximation in evolutionary algorithms for the optimization of costly functions
IEEE Transactions on Evolutionary Computation
Implementing linear models in genetic programming
IEEE Transactions on Evolutionary Computation
Max-min surrogate-assisted evolutionary algorithm for robust design
IEEE Transactions on Evolutionary Computation
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
Nonlinear Least Square Regression by Adaptive Domain Method With Multiple Genetic Algorithms
IEEE Transactions on Evolutionary Computation
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
The Journal of Machine Learning Research
Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments
SIAM Journal on Scientific Computing
Acute leukemia classification by ensemble particle swarm model selection
Artificial Intelligence in Medicine
Automatic surrogate model type selection during the optimization of expensive black-box problems
Proceedings of the Winter Simulation Conference
An optimization algorithm employing multiple metamodels and optimizers
International Journal of Automation and Computing
Efficient global optimization algorithm assisted by multiple surrogate techniques
Journal of Global Optimization
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Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist (Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm (heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type.