  Ghost imaging can capture 2D images with a point detector instead of an array
sensor. It therefore offers a solution to the challenge of building area format
sensors in wavebands where such sensors are difficult and expensive to produce
and opens up new imaging modalities due to high-performance single-pixel
detectors. Traditionally, ghost imaging retrieves the image of an object
offline, by correlating measured light intensities and applied illuminating
patterns. Here we present a feedback-based approach for online updating of the
imaging result that can bypass post-processing, termed self-evolving ghost
imaging (SEGI). We introduce a genetic algorithm to optimize the illumination
patterns in real-time to match the objects shape according to the measured
total light intensity. We theoretically and experimentally demonstrate this
concept for static and dynamic imaging. This method opens new perspectives for
real-time ghost imaging in applications such as remote sensing (e.g. machine
vision, LiDAR systems in autonomous vehicles) and biological imaging.
