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Boomerang-shaped neural embeddings for NK landscapes


Understanding the landscape underlying NK models is of fundamental interest. Different representations have been proposed to better understand how the ruggedness of the landscape is influenced by the model parameters, such as the problem dimension, the degree of non-linearity and the structure of variable interactions. In this paper, we propose to use neural embedding, that is a continuous vectorial representation obtained as a result of applying a neural network to a prediction task, in order to investigate the characteristics of NK landscapes. The main assumption is that neural embeddings are able to capture important features that reflect the difficulty of the landscape. We propose a method for constructing NK embeddings, together with metrics for evaluating to what extent this embedding space encodes valuable information from the original NK landscape. Furthermore, we study how the embedding dimensionality and the parameters of the NK model influence the characteristics of the NK embedding space. Finally, we evaluate the performance of optimizers that solve the continuous representations of NK models by searching for solutions in the embedding space.
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Dates and versions

hal-03693668 , version 1 (02-03-2023)



Roberto Santana, Arnaud Liefooghe, Bilel Derbel. Boomerang-shaped neural embeddings for NK landscapes. GECCO 2022 - Genetic and Evolutionary Computation Conference, Jul 2022, Boston, MA, United States. pp.858-866, ⟨10.1145/3512290.3528856⟩. ⟨hal-03693668⟩
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