Genetic Algorithm Parameter Control: Application to Scheduling with Sequence-Dependent Setups

Genetic algorithms, and other forms of evolutionary computation, are controlled by numerous parameters, such as crossover and mutation rates, population size, among others depending upon the specific form of evolutionary computation as well as which operators are employed. Setting the values for these parameters is no simple task. In this paper, we develop a genetic algorithm with adaptive control parameters for an NP-Hard scheduling problem known as weighted tardiness scheduling with sequence-dependent setups. Our genetic algorithm uses the permutation representation along with the non-wrapping order crossover and insertion mutation operators. We encode the control parameters within the members of the population and evolve these during search using Gaussian mutation. We demonstrate this approach out-performs a manually tuned genetic algorithm for the problem, and that it converges upon effective parameter values very early in the run.