| Curve and surface representations and their geometry processing including intersections have been the subject of significant research since the advent of computers in engineering design. This dissertation presents a genetic algorithm approach to curve and surface intersection. The main idea is explained as follows: some number of points are distributed on the curve and on the surface. These points are then randomly grouped together to form couples. A genetic algorithm is used to minimize the distance that each couple represents.; The proposed method offers many advantages. It is simple, general, accurate, fast, and robust. The method is insensitive to special cases such as tangential curves and surfaces. In addition, it works with different sizes, opposite parameterization directions, and self-intersecting curves or surfaces. The algorithm mainly involves evaluating points, computing the distance between two points, and converting binary strings to decimal values, and vice versa.; The offered approach has a few disadvantages. Sometimes, it may miss some intersection points, may take different times on different runs, and relies on experimental parameters.; The importance of this research lies in the numerous applications where the solutions could apply. A practical solution to the intersection problem may have substantial effect on the areas of solid modeling, robotics, space exploration, and manufacturing in general. |
