Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization

[1]  Thomas Bäck,et al.  Metamodel-Assisted Evolution Strategies , 2002, PPSN.

[2]  Kok Wai Wong,et al.  Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems , 2005 .

[3]  Michèle Sebag,et al.  Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es) , 2013, GECCO '13.

[4]  Martin Holena,et al.  Combinatorial Development of Solid Catalytic Materials: Design of High-Throughput Experiments, Data Analysis, Data Mining , 2009 .

[5]  Yaochu Jin,et al.  Knowledge incorporation in evolutionary computation , 2005 .

[6]  Anne Auger,et al.  COCO: a platform for comparing continuous optimizers in a black-box setting , 2016, Optim. Methods Softw..

[7]  Jakub Repický,et al.  Gaussian Process Surrogate Models for the CMA Evolution Strategy , 2019, Evolutionary Computation.

[8]  Gabriel Kronberger,et al.  Evolution of Covariance Functions for Gaussian Process Regression Using Genetic Programming , 2013, EUROCAST.

[9]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[10]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[11]  Anne Auger,et al.  Benchmarking the local metamodel CMA-ES on the noiseless BBOB'2013 test bed , 2013, GECCO.

[12]  N. Hansen.  A global surrogate assisted CMA-ES , 2019, GECCO.

[13]  Bin Li,et al.  An evolution strategy assisted by an ensemble of local Gaussian process models , 2013, GECCO '13.

[14]  Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods , 2020, ECML/PKDD.

[15]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[16]  Günter Rudolph,et al.  Investigating uncertainty propagation in surrogate-assisted evolutionary algorithms , 2017, GECCO.