`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
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
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Top 10 algorithms in data mining
Knowledge and Information Systems
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
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
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
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The modern engineering design optimization process often replaces laboratory experiments with computer simulations, which leads to expensive black-box optimization problems. Such problems often contain candidate solutions which cause the simulation to fail, and therefore they will have no objective value assigned to them, a scenario which degrades the search effectiveness. To address this, this paper proposes a new computational intelligence optimization algorithm which incorporates a classifier into the optimization search. The classifier predicts which solutions are expected to cause a simulation failure, and its prediction is used to bias the search towards solutions for which the simulation is expected to succeed. To further enhance the search effectiveness, the proposed algorithm continuously adapts during the search the type of model and classifier being used. A rigorous performance analysis using a representative application of airfoil shape optimization shows that the proposed algorithm outperformed existing approaches in terms of the final result obtained, and performed a search with a competitively low number of failed evaluations. Analysis also highlights the contribution of incorporating the classifier into the search, and of the model and classifier selection steps.