Academia.eduAcademia.edu

Figure 2 – uploaded by Sandor Markon

See full PDFdownloadDownload figure
Table 1. Specifications of EGSCS Simulator Table 2. Ratio of Passengers in Each Traffic Flow

Table 1 Specifications of EGSCS Simulator Table 2. Ratio of Passengers in Each Traffic Flow

Related Figures (9)

Fig.1. Basic Structure of an EGSCS using GNP with RL Keywords: elevator group supervisory control system, genetic network programming, reinforcement learning, impor- tance weight
Fig.1. Basic Structure of an EGSCS using GNP with RL Keywords: elevator group supervisory control system, genetic network programming, reinforcement learning, impor- tance weight
Fig. 2. Flowchart of evolution and learning
Fig. 2. Flowchart of evolution and learning
Fig.4. Basic Structure of an EGSCS using GNP with RL
Fig.4. Basic Structure of an EGSCS using GNP with RL
Table 3. Parameters for GNP with RL 4. Simulation Results
Table 3. Parameters for GNP with RL 4. Simulation Results
Fig.5. Fitness Curves in each Traffic Flow one kind of judgment node and two kinds of processing nodes, with 10 nodes of each kind. Moreover, the node size is also fixed for simplicity.
Fig.5. Fitness Curves in each Traffic Flow one kind of judgment node and two kinds of processing nodes, with 10 nodes of each kind. Moreover, the node size is also fixed for simplicity.
Table 4. Performance Comparison of Different Methods in Simulations
Table 4. Performance Comparison of Different Methods in Simulations
Japan and IEEE.
Japan and IEEE.
Related topics:
Information SystemsEngineeringComputer ScienceReinforcement LearningDynamic OptimizationGenetic Network Programming
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved