2.5.8.      Genetic Algorithm

The Genetic Algorithm (GA) for Accelerometer Placement is a Matlab-based program that will optimally select accelerometer locations from a candidate set of DOF. This iterative process replicates the way a new generation of gene sequences is produced through direct reproduction, crossover, and mutation from an existing population. By this process a set of accelerometer locations can be efficiently selected to maximize the linear independence of test-measured modeshapes. The GA can also accommodate multiple FEM configurations, simultaneously selecting the best accelerometer locations for multi-configuration modal tests to minimize the setup time.

The foundation for the Genetic Algorithm is based on the principle of survival of the fittest. It uses the analogy of the evolution of a gene sequence over several generations to yield the optimal accelerometer set. In this analogy each gene represents a single accelerometer location, a gene sequence represents a set of accelerometer locations, and the population consists of several different accelerometer sets. At every generation an objective function is used to evaluate each accelerometer set’s fitness, and those with the highest fitness (or lowest error) have the highest probability of reproducing to form the next generation. Three forms of reproduction are implemented: direct reproduction, crossover, and mutation.

For a more thorough discussion of the Genetic Algorithm, refer to the documentation available here.