Improved genetic operators for structural engineering optimization

The initial motivation for the development of algorithms inspired by biological 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 population genetics and natural selection. Their growing popularity may be attributed to the ability of GAs as powerful function optimizers of general application to combinatorial problems that have been traditionally difficult to optimize. (De Jong, K. A. and Spears, W. M., Using genetic algorithms to solve NP-complete problems. In Proceedings of the Third International Conference on Genetic Algorithms, June 1989, pp. 124-132; Hurley, S., Using Genetic Algorithms Based Search in Optimization. The Institute of Mathematics and its Applications, Vol. 29, March/April 1993, pp. 43-46.) Considerable progress has been made in identifying the limitations of the GAs resulting in a range of approaches and modifications which attempt to improve the efficiency of the GAs as function optimizers. These adaptive approaches in such GA-based optimizers are in general tailored to classes of functions. The engineering optimization problems may be governed by different classes of functions which result in very complex design spaces. In this paper a general purpose optimization technique is investigated, the best of the traditional methods may perform well but only in a narrow class of problems. Revised genetic operators and a new recombination scheme are presented 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 designed to ensure the balance between effective exploration and selective pressure to exploit the better solutions which are the main power behind the GAs. The gain of exploratory power not only extends the applicability of the method and improves the quality of the results but also helps prevent premature convergence. On the other hand, selective pressure applied locally may speed up the convergence while still refining the results. Finally, in order to map GAs onto engineering optimization problems, this paper draws some guidelines for handling the constraints using transformation methods.