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
2 School of Computer and
Information Science
Communication
Linköping University
Lanzhou University
Sweden
China
Motivation
 FlexRay
 Safety-critical applications in the static segment
 Other applications in the dynamic segment
Many optimization
parameters
2
Outline
 System model
 Bus cycle of FlexRay
 Problem formulation
 Optimization with immune genetic algorithms
 Experimental results
3
System model
4
FlexRay configuration
Static
phase
Dynamic phase
a
c
Bus
1 cycle2
3
1
d
b
2
3
 Frame identifiers and
priorities to messages
 Priorities to tasks
a
1
b
c
3
d
2
5
Timing with some configuration
Average = 477
6
Timing with some other configuration
Average = 369
Previous case:
Average = 477
7
Problem formulation
 Parameters:
 Priorities of the tasks
 Frame identifiers and priorities of the messages
 Objective:
 Minimize the average response time of tasks
8
Immune genetic algorithms
Crossover
Initial population
3
Evaluate costs
1
2
4 3 2 3 3
Mutation
Simulation
of
each member
1
2
Population
Population costs
Vaccination
Stop?
No
New population
9
Vaccination
Population
3
1
4
2
4
3
2
3
3
1
2
1
4
2
3
3
3
3
1
3
2
2
4
1
3
1
3
1
1
2
1
3
1
3
2
4
3
1
3
2
3
2
1
3
4
2
1
3
4
2
2
1
3
4
2
3
4
2
1
80 40 60
60
60 80
Create vaccines
Dominance
threshold 50%
10
Vaccination
Population
Vaccination
rate
Create vaccines
Select member
Member
Vaccine set
Select vaccines
Vaccines
Vaccinate
11
Vaccination
2
3
1
4
1
2
1
2
1
3
Member
4
Vaccines
2
2
4
1
3
1
2
2
2
1
3
New member
12
Vaccination
Population
Vaccination
rate
Create vaccines
Select member
Dominance
Vaccine set
threshold
No
Member
Last
member
?
Select vaccines
Vaccines
Yes
Vaccinate
New population
13
Cost
Tuning – Vaccination rate
Vaccination rate [%]
14
Cost
Tuning – Dominance threshold
Dominance threshold [%]
15
Vaccination
 Takes advantage of local properties of good
solutions
 Speed up the optimization process
 Improve the quality of the final solution
16
Cost improvements [%]
Experiments – Improvements
GA
IGA
Number of tasks
17
Runtimes [seconds]
Experiments – Runtime
GA
IGA
Number of tasks
18
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)
19