Parallel genetic models for structural optimization

Genetic Algorithm (GA) based optimizers are adaptive search algorithms that combine principles of population genetics and natural selection. These algorithms have been successfully applied to several optimization problems which are difficult to solve by conventional mathematical programming. In engineering, GAs are rapidly becoming an important tool for general purpose optimization because the best traditional methods may only perform well within a narrow class of problems. However, in the case of small to medium size problems, GA-based optimizers are generally out-performed by conventional optimizers in terms of computational effort. In order to circumvent this problem, a number of parallel Genetic Algorithms (pGAs) have already been proposed and analysed for different types of functions. In general, these pGAs have been tested on unconstrained optimization which requires function evaluations of a relatively low cost. This paper considers the evaluation of pGAs for engineering problems where function evaluations vary from medium to high cost and the solution spaces are very complex and highly constrained.