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Found 4 papers, 0 papers with code
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
To this end, we develop a multi-objective Bayesian evolutionary optimization approach to HE-MOPs by exploiting the different data sets on the cheap and expensive objectives in HE-MOPs to alleviate the search bias caused by the heterogeneous evaluation costs for evaluating different objectives.
Recent Advances in Bayesian Optimization
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency.
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time.
Learning pose variations within shape population by constrained mixtures of factor analyzers
The segmentation is obtained by components posterior probabilities and the rotations in pose variations are learned by the factor loading matrices.
