An a-priori Parameter Selection Approach to Enhance the Performance of Genetic Algorithms Solving Pickup and Delivery Problems

Solving a pickup and delivery problem with, e. g., multiple depots, time windows, and heterogeneous vehicles is a challenging routing task. Due to the complexity, a meta-heuristic approach (e. g., a genetic algorithm) with sufficiently good solution quality is recommended. Genetic algorithms contain multiple operators such as the crossover and mutation operators that are called with certain probabilities. However, selecting appropriate probability values (parameters) for these operators strongly depend on the data structure of the given instances. For each new instance, the best parameter configuration must be found to enhance the overall solution quality. In this paper, an a-priori parameter selection approach based on classifying new instances to clusters is presented. Beforehand, a bayesian optimization approach with gaussian processes is used to find the best parameters for each cluster. The a-priori parameter selection is evaluated on four well-known pickup and delivery problem data sets, each with 60 instances and different number of depots.