Trust-region methods
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Metamodel-Assisted Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
A framework for memetic optimization using variable global and local surrogate models
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Handling undefined vectors in expensive optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Accelerating evolutionary algorithms with Gaussian process fitness function models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
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The modern engineering design process often relies on computer simulations to evaluate candidate designs. This simulation-driven approach results in what is commonly termed a computationally expensive black-box optimization problem. In practise, there will often exist candidate designs which cause the simulation to fail. Such simulation failures can consume a large portion of the allotted computational resources, and thus can lead to search stagnation and a poor final solution. To address this issue, this study proposes a new computational intelligence optimization algorithm which combines a model and a k-NN classifier. The latter predicts which solutions are expected to cause the simulation to fail, and its prediction is incorporated with the model prediction to bias the search towards valid solutions, namely, for which the simulation is expected to succeed. A main contribution of this study is that to further improve the search efficacy, the proposed algorithm leverages on model-selection theory and continuously calibrates the classifier during the search. An extensive performance analysis using an engineering application of airfoil shape optimization shows the efficacy of the proposed algorithm.