An engineering study of genetic algorithms oriented to geometric design applications
Zhenmin Zhu, Y. Chan · 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization · 1998
An extensive investigation on efficiency of a simple genetic algorithm (SGA) and a geometric genetic algorithm (GGA)[I1 is made based on the Elementary Probability theory and a one-dimensional model. It is found that GGA improves convergence as well as accuracy. The numerical results of two-dimensional geometric optimization problems confirm the analysis. In addition, the comparison among SGA, a random search and Newton's method is also included. The advantages and disadvantages of genetic algorithms are discussed in detail.