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DTSTART;TZID=Europe/Stockholm:20190612T111500
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UID:submissions.pasc-conference.org_PASC19_sess108_pap_jan130@linklings.co
 m
SUMMARY:(mu, lambda)-CCMA-ES for Constrained Optimization with an Applicat
 ion in Pharmacodynamics
DESCRIPTION:Paper\nComputer Science and Applied Mathematics, Life Sciences
 \n\n(mu, lambda)-CCMA-ES for Constrained Optimization with an Application 
 in Pharmacodynamics\n\nArampatzis, Wälchli, Weber, Rästas, Koumoutsakos\n\
 nWe present the algorithm CCMA, an extension to CMA-ES, an evolution strat
 egy that has shown to perform well in a broad range of black-box optimizat
 ion problems. The (mu,lambda)-CMA-ES effectively handles nonlinear nonconv
 ex problems but faces difficulties in constrained optimization problems. W
 e introduce viability boundaries and  normal approximations to treat 
 inequality constraints and improve the search for an initial point not con
 flicting with given constraints. We compare the performance of CCMA with a
  state of the art optimization algorithm (mViE)  showing favorable re
 sults. Finally, CCMA is applied to a pharmacodynamics problem describing t
 umor growth, and we demonstrate that the number of required model evaluati
 ons is reduced by an order of magnitude when compared with a state of the 
 art optimization algorithm.<br /><br />Full paper: https://doi.org/10.1145
 /3324989.3325725
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