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- W2813883314 abstract "To date, efforts to automatically configure problem representations for classes of optimization problems have yielded few practical results. We show that a recently proposed approach for training neural G-P maps for optimization problems yields maps that generalize poorly to translated problem instances. We propose that alternative neural architectures---especially ones that allow the number of control genes to be greater than the number of phenotypic traits---may provide a means of learning maps that are better able to generalize to new problem instances." @default.
- W2813883314 created "2018-07-19" @default.
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- W2813883314 creator A5077281790 @default.
- W2813883314 date "2018-07-06" @default.
- W2813883314 modified "2023-09-26" @default.
- W2813883314 title "Toward learning neural network encodings for continuous optimization problems" @default.
- W2813883314 cites W148981569 @default.
- W2813883314 cites W1565964788 @default.
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- W2813883314 doi "https://doi.org/10.1145/3205651.3205687" @default.
- W2813883314 hasPublicationYear "2018" @default.
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