Search Space Scaling in Genetic Algorithm-Based Inverse Analyses

Abstract Inverse problems arise at all levels of manufacturing organization through the need to estimate system parameters and optimize performance. In modelling the physics of various manufacturing processes, inverse analysis is primarily used to estimate boundary conditions at the part/machine interface. The quality of such estimates is influenced by the measurement process, the physics model, and the inverse analysis mechanism. Of the available mechanisms, genetic algorithms (GAs) are unusual because they do not rely on the use of sensitivity coefficients and they are easily integrated with direct models of process physics. Primary factors that influence GA performance are the topology of the search space and the way that this is sampled by the GA. Search space topology is dictated by comparison of the measured and calculated effects of the sought boundary conditions. Search space sampling is controlled by chosen chromosome structure and previous evolutionary progress. A method of dynamic search space scaling is discussed here, in which contractions of search space dimensions follow evolutionary progress and are triggered by changing population diversity within the GA. In comparison to evolution in a fixed search space, it is shown that dynamic search space scaling can be efficient in producing highly accurate estimates.