TY - GEN
T1 - Classifier-assisted optimization
AU - Tenne, Yoel
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/5/18
Y1 - 2016/5/18
N2 - Engineering design optimization often uses computer simulations to evaluate candidate designs. Such numerical simulations may consistently fail for some designs, but the failure reason being unknown. If such failures are frequent than the effectiveness of the optimization process can severely degrade. To address this issue this study describes the integration of classifiers, borrowed from the domain of machine learning, into the optimization search. The classifiers attempt to predict if a candidate design will cause a simulation crash, and this prediction is then used to bias the search. The effectiveness of the approach is demonstrated through several numerical experiments.
AB - Engineering design optimization often uses computer simulations to evaluate candidate designs. Such numerical simulations may consistently fail for some designs, but the failure reason being unknown. If such failures are frequent than the effectiveness of the optimization process can severely degrade. To address this issue this study describes the integration of classifiers, borrowed from the domain of machine learning, into the optimization search. The classifiers attempt to predict if a candidate design will cause a simulation crash, and this prediction is then used to bias the search. The effectiveness of the approach is demonstrated through several numerical experiments.
KW - Black-box optimization
KW - Machine learning
KW - Metamodels
UR - http://www.scopus.com/inward/record.url?scp=84986601808&partnerID=8YFLogxK
U2 - 10.1145/2903220.2903251
DO - 10.1145/2903220.2903251
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AN - SCOPUS:84986601808
T3 - ACM International Conference Proceeding Series
BT - 9th Hellenic Conference on Artificial Intelligence, SETN 2016
A2 - Bikakis, Antonis
A2 - Vrakas, Dimitrios
A2 - Bassiliades, Nick
A2 - Vlahavas, Ioannis
A2 - Vouros, George
T2 - 9th Hellenic Conference on Artificial Intelligence, SETN 2016
Y2 - 18 May 2016 through 20 May 2016
ER -