IMPROVED GENETIC OPERATORS FOR STRUCTURAL-ENGINEERING OPTIMIZATION

The initial motivation for the development of algorithms inspired by b iological principles of evolution was the design and implementation of robust adaptive systems. Among the most utilized of these techniques are the Genetic Algorithms (GAs) which combine principles of populatio n genetics and natural selection. Their growing popularity may be attr ibuted to the ability of GAs as powerful function optimizers of genera l application to combinatorial problems that have been traditionally d ifficult to optimize.(1,2) (De Jong, K. A. and Spears, W. M., Using ge netic algorithms to solve NP-complete problems. In Proceedings of the Third International Conference on Genetic Algorithms, June 1989, pp. 1 24-132; Hurley, S., Using Genetic Algorithms Based Search in Optimizat ion. The Institute of Mathematics and its Applications, Vol. 29, March /April 1993, pp. 43-46.) Considerable progress has been made in identi fying the limitations of the GAs resulting in a range of approaches an d modifications which attempt to improve the efficiency of the GAs as function optimizers. These adaptive approaches in such GA-based optimi zers are in general tailored to classes of functions. The engineering optimization problems may be governed by different classes of function s which result in very complex design spaces. In this paper a general purpose optimization technique is investigated, the best of the tradit ional methods may perform well but only in a narrow class of problems. Revised genetic operators and a new recombination scheme are presente d in this paper. These features respectively increase the exploratory power of the GA while simultaneously introducing additional selection pressure to increase the speed of convergence. These features are desi gned to ensure the balance between effective exploration and selective pressure to exploit the better solutions which are the main power beh ind the GAs. The gain elf exploratory power not only extends the appli cability of the method and improves the quality of the results but als o helps prevent premature convergence. On the other hand, selective pr essure applied locally may speed up the convergence while still refini ng the results. Finally, in order to map GAs onto engineering optimiza tion problems, this paper draws some guidelines for handling the const raints using transformation methods. (C) 1998 Elsevier Science Limited and Civil-Comp Limited. All rights reserved.