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The most popular representations for scheduling heuristics are those used to evolve the priority functions.
Hildebrandt and Branke [45] proposed a surrogate model based on the phenotypic characterisation of evolved priority functions.
Within the planning models, each participant's forest management goals are defined using additive priority functions consisting of weighted sub-utility and/or achievement functions.
Priority functions are used in a new approach for the computation of the shortcuts and three different variants are experimentally evaluated with respect to the approximation quality.
The experiments show that these extended representations can help GP evolve significantly better scheduling heuristics as compared to those focusing only on priority functions.
For example, Nguyen et al. [89] used grammars to evolve scheduling heuristics (as shown in Fig. 7) that can select appropriate priority functions based on the shop conditions.
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PRB is allocated to the flow maximizing the priority function.
In scheduling stage, a leakage-aware priority function is derived to cope with the leakage power.
However, the empirical parameters in the priority function are hard to appropriately choose.
In GEP, the priority function is represented as a chromosome of one or more genes.
For CABA, the weighted factor in the priority function may influence the performance.
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