Simple genetic algorithm parameter selection for protein structure prediction

Selection of run-time parameters is a critical step in the application of genetic algorithms (GAs). Numerous investigations have discussed parameter set selection, both theoretically and empirically. Theoretical work has focused on the choice of population size, while empirical studies cover a wide range of GA parameters. Theory suggests population sizes which increase exponentially with string length. The available experimental data suggests small populations perform consistently well, but the test problems are limited to small string lengths. Thus, we still do not have a complete understanding of how parameters should be chosen, especially for problems with large string lengths. This study extends Schaffer´s (1989) results by performing a similar empirical analysis of GA parameters on a real-world application (protein structure prediction), with longer string lengths and a very large number of local optima. Relationships between population size, mutation rates and crossover rates similar to those reported by Schaffer are shown