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Representing Experience in Continuous Evolutionary optimisation through Problem-tailored Search Operators.

, , , , and . CEC, page 1-7. IEEE, (2020)

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Learning Transferable Variation Operators in a Continuous Genetic Algorithm., , , , and . SSCI, page 2027-2033. IEEE, (2019)A new acquisition function for robust Bayesian optimization of unconstrained problems., , , , and . GECCO Companion, page 1344-1345. ACM, (2021)Exploring Dimensionality Reduction Techniques for Efficient Surrogate-Assisted optimization., , , , , and . SSCI, page 2965-2974. IEEE, (2020)Efficient AutoML via Combinational Sampling., , , , and . SSCI, page 1-10. IEEE, (2021)Target shape design optimization with evolutionary computation., , and . IEEE Congress on Evolutionary Computation, page 1864-1870. IEEE, (2003)Connectedness, regularity and the success of local search in evolutionary multi-objective optimization., and . IEEE Congress on Evolutionary Computation, page 1910-1917. IEEE, (2003)On the Behavior of (μ/μ1, λ)-ES Optimizing Functions Disturbed by Generalized Noise., , and . FOGA, page 307-328. Morgan Kaufmann, (2002)Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems., , , , and . SSCI, page 1-7. IEEE, (2021)Learning Time-Series Data of Industrial Design Optimization using Recurrent Neural Networks., , , , , , , and . ICDM Workshops, page 785-792. IEEE, (2019)Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models., , , , , and . IJCNN, page 1-9. IEEE, (2020)