Towards Adaptation in Multiobjective Evolutionary Algorithms for Integer Problems

Parameter control refers to the techniques that dynamically adapt the parameter values of the evolutionary algorithm during the optimization process, such as population size, crossover rate, or operator selection. Adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. With this article, we contribute a systematic investigation of an adaptive, multi-objective algorithm that is designed for the optimisation of problems in unbounded integer decision spaces. We find that (1) adaptation outperforms the best static configurations by 39–82 %, and (2) performance of the multi-objective algorithm is often independent of the adaptation scheme's initial configuration.