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Modern engineering design optimization often evaluates candidate designs with computer simulations. In this setup, there will often exist candidate designs which cause the simulation to fail and would have no objective value assigned to them. This, in turn, can degrade the effectiveness of the design optimization process and lead to a poor final result. To address this issue, this paper proposes a new computational intelligence optimization algorithm which incorporates a classifier into the optimization process. The latter predicts which candidate designs are expected to cause a simulation failure, and its prediction is used to bias the search towards candidate designs for which the simulation is expected to succeed.
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