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)
On the use of self-organizing maps for clustering and visualization
Intelligent Data Analysis
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
A computational intelligence algorithm for simulation-driven optimization problems
Advances in Engineering Software
A classifier-assisted framework for expensive optimization problems: a knowledge-mining approach
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A computational intelligence algorithm for expensive engineering optimization problems
Engineering Applications of Artificial Intelligence
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When using computer simulations in engineering design optimization one often encounters vectors which ‘crash’ the simulation and so no fitness is associated with them. In this paper we refer to these as undefined vectors since the objective function is undefined there. Since each simulation run (a function evaluation) is expensive (anywhere from minutes to weeks of CPU time) only a small number of evaluations are allowed during the entire search and so such undefined vectors pose a risk of consuming a large portion of the optimization ‘budget’ thus stalling the search. To manage this open issue we propose a classification-assisted framework for expensive optimization problems, that is, where candidate vectors are classified in a pre-evaluation stage whether they are defined or not. We describe: a) a baseline single-classifier framework (no undefined vectors in the model) b) a non-classification assisted framework (undefined vectors in the model) and c) an extension of the classifier-assisted framework to a multi-classifier setup. Performance analysis using a test problem of airfoil shape optimization shows: a) the classifier-assisted framework obtains a better solution compared to the non-classification assisted one and b) the classifier can data-mine the accumulated information to provide new insights into the problem being solved.