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• W1567473651 abstract "This paper proposes a simple modification of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for high dimensional objective functions, reducing the internal time and space complexity from quadratic to linear. The covariance matrix is constrained to be diagonal and the resulting algorithm, sep-CMA-ES, samples each coordinate independently. Because the model complexity is reduced, the learning rate for the covariance matrix can be increased. Consequently, on essentially separable functions, sep-CMA-ES significantly outperforms CMA-ES. For dimensions larger than a hundred, even on the non-separable Rosenbrock function, the sep-CMA-ES needs fewer function evaluations than CMA-ES." @default.
• W1567473651 created "2016-06-24" @default.
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• W1567473651 date "2008-01-01" @default.
• W1567473651 modified "2023-09-28" @default.
• W1567473651 title "A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity" @default.
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• W1567473651 doi "https://doi.org/10.1007/978-3-540-87700-4_30" @default.
• W1567473651 hasPublicationYear "2008" @default.
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