author = {Nicolas Boria and Sébastien Bougleux and Luc Brun}, title = {Approximating GED using a Stochastic Generator and Multistart IPFP}, booktitle = {Proceedings of SSPR'2018}, year = 2018, pages= "460--469", month = {August}, organization = {IAPR}, publisher = {Springer International Publishing}, editor="Bai, Xiao and Hancock, Edwin R. and Ho, Tin Kam and Wilson, Richard C. and Biggio, Battista and Robles-Kelly, Antonio", theme={pattern,ged}, url={HAL:=https://hal-normandie-univ.archives-ouvertes.fr/hal-01865351, HAL(PDF):= https://hal-normandie-univ.archives-ouvertes.fr/hal-01865351/document}, abstract={ The Graph Edit Distance defines the minimal cost of a sequence of elementary operations transforming a graph into another graph. This versatile concept with an intuitive interpretation is a fundamental tool in structural pattern recognition. However, the exact computation of the Graph Edit Distance is N P-complete. Iterative algorithms such as the ones based on Franck-Wolfe method provide a good approximation of true edit distance with low execution times. However, underlying cost function to optimize being neither concave nor convex, the accuracy of such algorithms highly depends on the initialization. In this paper, we propose a smart random initializer using promising parts of previously computed solutions.}, isbn="978-3-319-97785-0"