Genetic algorithms and simulated annealing for robustness analysis

Genetic algorithms (GAs) and simulated annealing (SA) have been promoted as useful, general tools for nonlinear optimization. This paper explores their use in robustness analysis with real parameter variations, a known NP hard problem which would appear to be ideally suited to demonstrate the power of GAs and SA. Numerical experiment results show convincingly that they turn out to be poorer than existing branch and bound (B&B) approaches. While this may appear to shed doubt on some of the hype surrounding these stochastic optimization techniques, we find that they do have attractive features, which are also demonstrated in this study. For example, both GAs and SA are almost trivial to understand and program, so they require essentially no expertise, in sharp contrast to the B&B methods. They may be suitable for problems where programming effort is much more important than running time or the quality of the answer. Robustness analysis for engineering problems is not the best candidate in this respect, but it does provide an interesting test case for the evaluation of GAs and SA. A simple hill climbing algorithm is also studied for comparison