Genetic Programming for Dynamic Flexible Job Shop Scheduling: Evolution With Single Individuals and Ensembles

Dynamic flexible job shop scheduling is an important but difficult combinatorial optimization problem that has numerous real-world applications. Genetic programming (GP) has been widely used to evolve scheduling heuristics to solve this problem. Ensemble methods have shown promising performance in many machine learning tasks, but previous attempts to combine GP with ensemble techniques are still limited and require further exploration. This article proposes a novel ensemble GP (EGP) method that uses a population consisting of both single individuals and ensembles. The main contributions include: 1) developing a GP method that evolves a population comprising both single individuals and ensembles, allowing breeding between them to explore the search space more effectively; 2) proposing an ensemble construction and selection strategy to form ensembles by selecting diverse and complementary individuals; and 3) designing new crossover and mutation operators to produce offspring from single individuals and ensembles. Experimental results demonstrate that the proposed method outperforms existing traditional and EGP methods in most scenarios. Further analyses find that the success is attributed to the enhanced population diversity and extensive search space exploration achieved by the proposed method.