Abstract
A learning-based exploration approach is proposed to escape from the basins of attraction of converged-to optima, by selecting on what is termed the interestingness of a solution. This interestingness is based on the modeling error made by a surrogate model that is trained on all solutions encountered earlier during the search. Compared to multiple standard optimization runs, a learning-guided restart scheme that alternates between a quality optimization phase and an exploration phase directed by interestingness finds solutions that are more diverse and of higher quality.
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Reehuis, E., Olhofer, M., Sendhoff, B., Bäack, T. (2013). Learning-Guided Exploration in Airfoil Optimization. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_61
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DOI: https://doi.org/10.1007/978-3-642-41278-3_61
Publisher Name: Springer, Berlin, Heidelberg
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