Niching-Based Feature Selection with Multi-tree Genetic Programming for Dynamic Flexible Job Shop Scheduling

Genetic programming has been explored in recent works to evolve hyperheuristics for dynamic flexible job shop scheduling. To generate optimum rules, the algorithm searches a space of trees composed from a set of terminals and operators. Since the search space is exponentially proportional to the size of the terminal set, it is preferred to opt out any insignificant terminals. Feature selection techniques has been employed to reduce the terminal set size without discarding any important information and they have proven to be effective for enhancing search performance and efficiency for dynamic flexible job shop scheduling. In this paper, we extends our previous work by adding a modified version of the two-stage genetic programming algorithm and by comparing the different methods in a larger experimental setup. The results show that feature selection can generate better rules in most of the cases while also being more efficient to in a production environment.