Evolutionary computation offers many interesting algorithms in which the behavior of a group of organisms or elements seems to have some fundamentally distinct collective intelligence. This collective intelligence allows that very simple elements can form capable systems to solve highly complex tasks by interacting to each other. On the other hand, automatic circle detection in digital images has been considered as an important and complex task for the computer vision community that has devoted a tremendous amount of research seeking for an optimal circle detector. This chapter presents an algorithm for the automatic detection of circular shapes embedded into cluttered and noisy images with no consideration of conventional Hough transform techniques. The algorithm uses the encoding of three non-collinear points embedded into an edge-only image as candidate circles. Guided by the values of the objective function, the set of encoded candidate circles (charged particles) are evolved using the EMO algorithm so that they can fit into the actual circular shapes on the edge map of the image. Experimental results from several tests on synthetic and natural images with a varying range of complexity are included to validate the efficiency of the presented technique regarding accuracy, speed, and robustness.