@inproceedings {PUBA293,
	title = {Learning-guided Exploration in Airfoil Optimization},
	author = {Edgar Reehuis AND Markus Olhofer AND Bernhard Sendhoff AND Thomas B{\"a}ck},
	year = {2013},
	month = {October},
	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.},
	publisher = {Springer},
	booktitle = {International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2013)}
}