1. ขั้นตอนวิธีเชิงพันธุกรรมสำหรับการอนุมานเครื่องจักรสถานะจำกัด
อาจารย์ที่ปรึกษาวิทยานิพนธ์
รศ. ดร. ประภาส จงสถิตย์วัฒนา
ประธานกรรมการ
ศ. ดร. ชิดชนก เหลือสินทรัพย์
กรรมการ
ผศ. ดร. บุญเสริม กิจศิริกุล
ผศ. ดร. ณชล ไชยรัตนะ
เสนอโดย
นายนัทที นิภานันท์ เลขประจำตัว

2. Introduction Inference of FSM ≡ ? ? ? from observed input/output
Mimic the target machine
Target Machines
Hypothesis Machine
? ? ? ≡
INPUT
OUTPUT
Learning Method

3. Presentation Outline Claim Some details of claim Conviction Extras
Legal stuff
Some details of claim
Conviction
Experiment
Analysis
Conclusion
Extras
Summary

4. Hypothesis
A new genetic algorithm proposed in this thesis is a better way to solve the problem of finite state machine inference than the former genetic algorithm

5. Legal stuff: Objective
To develop a better genetic algorithm for the problem

6. Legal Stuff: Scope
Compare the new method with reference genetic algorithm
The new method must be shown to be better than the reference method
The solutions from the new method must be shown to be consistent

7. Former GA (REF) Encode δ and λ in bit string Single point crossover
Evaluate by counting similar output bit
...
Next State
Output
Next State
Output
Next State
Output
Next State
Output
0-transition
1-transition
0-transition
1-transition
State 0
State N
Hypothesis Machine
Hypothesis OUTPUT
INPUT Sequence
Compare
OUTPUT Sequence
OUTPUT Sequence

8. New GA New evaluation & encoding New crossover operator NEW1 method

9. New Evaluation Old evaluation can mislead the search
0/B
Old evaluation can mislead the search
Correct δ under wrong λ will result in totally wrong score B A
0/A
1/A
1/B
Target Machine
0/A B A
0/B
1/B
1/A
Hypothesis Machine

10. New Evaluation Main idea Perform local search on each output value
Each transition is evaluated by some IO pairs
Why make-then-ask?
Perform local search on each output value
old method fixes output (A) it is only 1/3 correct
IO Sequence:
Evaluate
particular state X by
(0,B) (0,B) (0,A)
0/A X
new method adjusts its output according to IO.
It choose B to get 2/3 correct
0/?  0/B X

11. New Evaluation: Example
(b) 1
(c)
Input :
Output :
a c d a d a d
(a) X Y
(d) 1
Evaluation value = = 6

12. Output Definition X Y (b) 1 (c) (a) (d) 1 Output:  0
(a) X Y
(d) 1
Output:
 0
 N/A (any arbitrary value)
 1

13. New Encoding Encoding λ is futile ... omitted Next State Next State
0-transition
1-transition
0-transition
1-transition
State 0
State N

14. New Crossover
The encoding scheme introduces chance of not having a tight linkage
high destructive effect A B C D E F G

15. New Crossover Choose two parents, find the best one
Rearrange state according to DFS order
discard inaccessible state
Perform single point crossover on the new list of states

16. New Crossover: ExampleB C D E F G A G C E D B

17. Experiment To compare performance of REF, NEW1 and NEW2
Measure number of generation used, time used and successful run

18. Setup of the experiment
Output
Algorithm
FSM Generator
Hypothesis Machine
REF
Hypothesis Machine
IO Sequence Generator
Hypothesis Machine
Target FSM
FSM
FSM
Hypothesis Machine
NEW1
Hypothesis Machine
Hypothesis Machine
IO Seq Set
FSM
FSM
Hypothesis Machine
NEW2
Hypothesis Machine
Hypothesis Machine
Input

19. Experimental Result

20. Experimental Result

21. Experimental Result : Summary
(Generation)
REF
NEW1
NEW2
Total
749,246
527,488
505,780
Relative
100%
70.40%
67.51%
Best 31 35
Successful Runs
461
561
585
(Time)
REF
NEW1
NEW2
Total
64,350
46,393
51,801
Relative
100%
72.10%
80.50%
Best 2 52 26

22. Analysis New evaluation & coding = local search Schema preservation
Search space reduction
Intron from inaccessible state
Schema preservation
Intron nullifying
intron from unexercised state

23. Search Space Reduction

24. Search Space Reduction

25. Search Space Reduction

26. Search Space Reduction : Summary
Experiment A A1 A2
Avg. Generation Used
70.40%
61.60%
54.12%
Avg. Time Used
72.09%
55.16%
49.43%
Number of problem Solved
121.69%
135.62%
150.64%

27. Additional Experiment
Comparison between NEW2 and heuristic based method, red-blue
compare correctness of result when the size of training set is reduced
using cross validation method
correctness = proportion of correctly identified data on test set
Training set : IO Sequence
length: 5  35 (step 2)
number: 6  36 (step 6)

28. Heuristic Method : red-blue
Using heuristic in search
fast
scalable
No restriction on the size of the hypothesis

29. Correctness vs. Sample Size

30. Additional Experiment: Analysis
shorter description of hypothesis is more preferable
Occam’s Razor

31. Size of hypothesis vs. Sample Size

32. What have been done?
A genetic algorithm for finite state machine inference problem is presented
It is empirically shown that the proposed method is better than former methods

33. What can be extended by others?
Practical Issues
Better linkage awareness
Chromosome representation
Theoretical Issues
Effect of intron
Formal analysis of preference of short hypothesis

34. What would you like to ask?