CULBERTSON, Bryan*; DOMINGUEZ, Mike; KOKOSKA, Mark; LIEW, Chun Wai; Lafayette College; Lafayette College; Lafayette College; Lafayette College: Optimizing Fish Swimming Models Using A Novel Genetic Algorithm

Modeling and analysis of the swimming mechanics of undulatory locomotion is one example of a class of morphological problems that require assigning appropriate values to a wide range of parameters. We have developed and applied a flexible and extensible computing framework to a computational model that represents the swimming motion of a sunfish to determine parameter values that minimize the difference between calculated motion generated by the model and actual motion deduced from video of the modeled fish swimming in a flume. The difficulties in optimizing this model for accuracy include the size of the search space, the interdependence of variables, and the number of unusable points. These obstacles manifest themselves in an irregularly shaped landscape that standard optimization algorithms cannot easily navigate. Our approach applies an iterative genetic algorithm that uses new patterns of convergence and exploration to quickly converge on a satisficing answer and then explore more possible solutions. One aspect of these patterns is a new clustering technique that prevents premature stagnation. Preliminary results indicate that our algorithm is potentially faster and can generate more accurate solutions than previous algorithms. This project is supported by NSF grant DBI-0442269.