An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling

Genetic programming based hyper-heuristic (GP-HH) approaches that evolve ensembles of dispatching rules have been effectively applied to dynamic job shop scheduling (JSS) problems. Ensemble GP-HH approaches have been shown to be more robust than existing GP-HH approaches that evolve single dispatching rules for dynamic JSS problems. For ensemble learning in classification, the design of how the members of the ensembles interact with each other, e.g., through various combination schemes, is important for developing effective ensembles for specific problems. In this paper, we investigate and carry out systematic analysis for four popular combination schemes. They are majority voting, which has been applied to dynamic JSS, followed by linear combination, weighted majority voting and weighted linear combination, which have not been applied to dynamic JSS. In addition, we propose several mea-sures for analysing the decision making process in the ensembles evolved by GP. The results show that linear combination is generally better for the dynamic JSS problem than the other combination schemes investigated. In addition, the different combination schemes result in significantly different interactions between the members of the ensembles. Finally, the analysis based on the measures shows that the behaviours of the evolved ensembles are significantly affected by the combination schemes. Weighted majority voting has bias towards single members of the ensembles. © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0

A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem

In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches