We investigate the use of genetic algorithms (GAs) in the framework of image primitives extraction (such as segments, circles, ellipses or quadrilaterals). This approach completes the well-known Hough Transform, in the sense that GAs are efficient when the Hough approach becomes too expensive in memory, i.e. when we search for complex primitives having more than 3 or 4 parameters.
Indeed, a GA is a stochastic technique, relatively slow, but which provides with an efficient tool to search in a high dimensional space. The philosophy of the method is very similar to the Hough Transform, which is to search an optimum in a parameter space. However, we will see that the implementation is different.
The idea of using a GA for that purpose is not new, Roth and Levine  have proposed a method for 2D and 3D primitives in 1992. For the detection of 2D primitives, we re-implement that method and improve it mainly in three ways:
by using distance images instead of directly using contour images, which tends to smoothen the function to optimize,
by using a GA-sharing technique, to detect several image primitives in the same step,
by applying some recent theoretical results on GAs (about mutation probabilities) to reduce convergence time.