Machine Learning
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Bayesian Optimization Algorithms for Multi-objective Optimization
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Computer experiments and global optimization
Computer experiments and global optimization
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Structure optimization of neural networks for evolutionary design optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
Proceedings of the 2006 conference on Integrated Intelligent Systems for Engineering Design
Active learning with statistical models
Journal of Artificial Intelligence Research
Foundations of Genetic Programming
Foundations of Genetic Programming
Multi-objective equivalent random search
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective GA optimization using reduced models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
Noisy Multiobjective Optimization on a Budget of 250 Evaluations
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Pareto-Based Multi-output Model Type Selection
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
On expected-improvement criteria for model-based multi-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Dominance-based pareto-surrogate for multi-objective optimization
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A novel sequential design strategy for global surrogate modeling
Winter Simulation Conference
Efficient Optimization of Reliable Two-Node Connected Networks: A Biobjective Approach
INFORMS Journal on Computing
A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments
SIAM Journal on Scientific Computing
Resampling methods for meta-model validation with recommendations for evolutionary computation
Evolutionary Computation
Automatic surrogate model type selection during the optimization of expensive black-box problems
Proceedings of the Winter Simulation Conference
Multi-objective optimization with surrogate trees
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In many practical engineering design and other scientific optimization problems, the objective function is not given in closed form in terms of the design variables. Given the value of the design variables, the value of the objective function is obtained by some numerical analysis, such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on. It may even be obtained by conducting a real (physical) experiment and taking direct measurements. Usually, these evaluations are considerably more time-consuming than evaluations of closed-form functions. In order to make the number of evaluations as few as possible, we may combine iterative search with meta-modeling . The objective function is modeled during optimization by fitting a function through the evaluated points. This model is then used to help predict the value of future search points, so that high performance regions of design space can be identified more rapidly. In this chapter, a survey of meta-modeling approaches and their suitability to specific problem contexts is given. The aspects of dimensionality, noise, expensiveness of evaluations and others, are related to choice of methods. For the multiobjective version of the meta-modeling problem, further aspects must be considered, such as how to define improvement in a Pareto approximation set, and how to model each objective function. The possibility of interactive methods combining meta-modeling with decision-making is also covered. Two example applications are included. One is a multiobjective biochemistry problem, involving instrument optimization; the other relates to seismic design in the reinforcement of cable-stayed bridges.