TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Wiegand, Stefan A1 - Igel, Christian A1 - Handmann, Uwe T1 - Evolutionary multi-objective optimization of neural networks for face detection JF - International Journal of Computational Intelligence and Applications N2 - For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g. in the Viisage-FaceFINDER® video surveillance system. We describe the optimisation of such a NN by a hybrid algorithm combining evolutionary multi-objective optimisation (EMO) and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy. We compare an EMO and a single objective approach, both with online search strategy adaptation. It turns out that EMO is preferable to the single objective approach in several respects. Y1 - 2004 UR - http://www.worldscientific.com/doi/abs/10. 1142/S1469026804001288 U6 - https://doi.org/10.1142/S1469026804001288 DO - https://doi.org/10.1142/S1469026804001288 VL - 2004 IS - 4(3) SP - 237 EP - 253 S1 - 16 ER -