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Immune Genetic Algorithms for Optimization of Task Priorities and FlexRay Frame Identifiers

Immune Genetic Algorithms for Optimization of Task Priorities and FlexRay Frame Identifiers. Soheil Samii 1 , Yanfei Yin 1,2 , Zebo Peng 1 , Petru Eles 1 , Yuanping Zhang 2. 1 Dept. of Computer and Information Science Linköping University Sweden. 2 School of Computer and Communication

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Immune Genetic Algorithms for Optimization of Task Priorities and FlexRay Frame Identifiers

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  1. Immune Genetic Algorithms for Optimization of Task Priorities and FlexRay Frame Identifiers Soheil Samii1, Yanfei Yin1,2, Zebo Peng1, Petru Eles1, Yuanping Zhang2 1 Dept. of Computer and Information Science Linköping University Sweden 2School of Computer and Communication Lanzhou University China

  2. Motivation • FlexRay • Safety-critical applications in the static segment • Other applications in the dynamic segment Many optimization parameters

  3. Outline • System model • Bus cycle of FlexRay • Problem formulation • Optimization with immune genetic algorithms • Experimental results

  4. System model

  5. FlexRay configuration Static phase Dynamic phase a c d b 2 3 1 2 3 Bus cycle 1 • Frame identifiers and priorities to messages • Priorities to tasks a b c d 1 3 2

  6. Timing with some configuration Average = 477

  7. Timing with some other configuration Average = 369 Previous case: Average = 477

  8. Problem formulation • Parameters: • Priorities of the tasks • Frame identifiers and priorities of the messages • Objective: • Minimize the average response time of tasks

  9. Immune genetic algorithms Crossover Initial population 3 1 2 4 3 2 3 3 1 2 Simulation of each member Mutation Evaluate costs Population Population costs Vaccination Stop? New population No

  10. Vaccination Population 4 Create vaccines 3 1 2 4 3 2 3 3 1 2 2 1 4 2 3 3 3 3 1 3 2 3 2 4 1 3 1 1 1 2 1 3 2 1 3 2 4 3 1 3 3 2 1 1 3 4 2 1 3 4 2 2 1 3 4 2 3 4 2 1 Dominance threshold 50% 60 80 80 40 60 60

  11. Vaccination Population Vaccination rate Create vaccines Select member Vaccine set Member Select vaccines Vaccines Vaccinate

  12. Vaccination Member 2 3 1 4 1 2 1 2 1 3 4 Vaccines 2 New member 2 4 1 3 1 2 2 2 1 3

  13. Vaccination Population Vaccination rate Create vaccines Dominance threshold Select member Vaccine set No Member Select vaccines Last member? Vaccines Yes Vaccinate New population

  14. Tuning – Vaccination rate Cost Vaccination rate [%]

  15. Tuning – Dominance threshold Cost Dominance threshold [%]

  16. Vaccination • Takes advantage of local properties of good solutions • Speed up the optimization process • Improve the quality of the final solution

  17. Experiments – Improvements Cost improvements [%] GA IGA Number of tasks

  18. Experiments – Runtime Runtimes [seconds] GA IGA Number of tasks

  19. Conclusions • Minimize delays in distributed embedded systems • Task priorities • Frame identifiers for FlexRay messages • Immune genetic algorithms • Vaccination results in better optimization in terms of time and solution quality (compared to traditional genetic algorithms)

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