Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling (JSS) is a challenging combinatorial optimization problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need be made simultaneously under the dynamic environments. Genetic programming (GP), as hyperheuristic approach, has been successfully used evolve heuristics for JSS. However, in traditional GP, recombination between parents may disrupt beneficial building blocks by choosing crossover points randomly. This article proposes recombinative mechanism provide guidance GP realize effective adaptive produce offspring. Specifically, we define novel measure importance of each subtree an individual, information utilized decide points. The proposed attempts improve quality offspring preserving promising one parent incorporating good from other. algorithm examined on six scenarios with different configurations. results show that significantly outperforms state-of-the-art algorithms most tested scenarios, terms both final test performance convergence speed. addition, rules obtained have interpretability.