1. Case Injected Genetic Algorithms
Sushil J. Louis
Genetic Algorithm Systems Lab (gaslab)
University of Nevada, Reno

2. Learning from Experience: Case Injected Genetic Algorithm Design of Combinational Logic Circuits
Sushil J. Louis
Genetic Algorithm Systems Lab (gaslab)
University of Nevada, Reno

3. Outline Motivation What is the technique? Is it useful? Results
Genetic Algorithm and Case-Based Reasoning
Is it useful?
Evaluate performance on Combinational Logic Design
Results
Conclusions

4. Outline Motivation What is the technique? Is it useful? Conclusions
Genetic Algorithm and Case-Based Reasoning
Is it useful?
Combinational Logic Design
Strike Force Asset Allocation
TSP
Scheduling
Conclusions

5. Genetic Algorithm Non-Deterministic, Parallel, Search
Poorly understood problems
Evaluate, Select, Recombine
Population search
Population member encodes candidate solution
Building blocks combine to make progress
More resistant to local optima
Iterative, requiring many evaluations

6. Motivation
Deployed systems are expected to confront and solve many problems over their lifetime
How can we increase genetic algorithm performance with experience?
Provide GA with a memory

7. Case-Based Reasoning
When confronted by a new problem, adapt similar (already solved) problem’s solution to solve new problem
CBR  Associative Memory + Adaptation
CBR: Indexing (on problem similarity) and adaptation are domain dependent

8. Case Injected Genetic AlgoRithm
Combine genetic search with case-based reasoning
Case-base provides memory
Genetic algorithm provides adaptation
Genetic algorithm generates cases
Any member of the GA’s population is a case

9. System

10. Related work Seeding:Koza, Greffensttette, Ramsey, Louis
Lifelong learning: Thrun
Key Differences
Store and reuse intermediate solutions
Solve sequences of similar problems

11. Combinational Logic Design
An example of configuration design
Given a function and a target technology to work with design an artifact that performs this function subject to constraints
Target technology: Logic gates
Function: Parity checking
Constraints: 2-D gate array

12. Encoding

13. Encoding

14. Parity Input 3-bit Parity 3-1 problem 000 001 1 010 011 100 101 110
001 1
010
011
100
101
110
111

15. Which cases to inject? Problem distance metric (Louis ‘97)
Domain dependent
Solution distance metric
Genetic algorithm encodings
Binary – hamming distance
Real – euclidean distance
Permutation – longest common substring …

16. Problem similarity

17. Lessons
Storing and Injecting solutions may not improve solution quality
Storing and Injecting partial solutions does lead to improved quality

18. OSSP Performance

19. Solution Similarity

20. Periodic Injection Strategies
Closest to best
Furthest from worst
Probabilistic closest to best
Probabilistic furthest from worst
Randomly choose a case from case-base
Create random individual

21. Setup 50, 6-bit combinational logic design problems
Randomly select and flip bits in parity output to define logic function
Compare performance
Quality of final design solution (correct output)
Time to this final solution (in generations)

22. Parameters Population size: 30 No of generations: 30
CHC (elitist) selection
Scaling factor: 1.05
Prob. Crossover: 0.95
Prob. Mutation: 0.05
Store best individual every generation
Inject every 5 generations (2^5 = 32)
Inject 3 cases (10%)
Multiple injection strategies
Averages over 10 runs

23. Problem distribution

24. Performance - Quality

25. Performance - Time

26. Injection Strategies

27. Solution distribution

28. Strike force asset allocation
Allocate platforms to targets
Dynamic
Changing Priority
Battlefield conditions
Popup
Weather …

29. Factors in allocation Pilot proficiency Asset suitability Priority
Risk
Route
Other assets (SEAD)
Weather

30. Maximize mission success
Binary encoding
Platform to multiple targets
Target can have multiple platforms
Dynamic battle-space
Strong time constraints

31. Setup 50 problems. 10 platforms, 40 assets, 10 targets
Each platform could be allocated to two targets
Problems varied in risk matrix
Popsize=80, Generations=80, Pc=1.0, Pm=0.05, probabilistic closest to best, injection period=9, injection % = 10% of popsize

32. Results

33. TSP
Find the shortest route that visits every city exactly once (except for start city)
Permutation encoding. Ex: 35412
Similarity metric: Longest common subsequence (Cormen et al, Introduction to Algorithms)
50 problems, move city locations

34. TSP performance

35. Scheduling Job shop scheduling problems Permutation encoding (Fang)
Similarity metric: Longest common subsequence (Cormen et al, Introduction to Algorithms)
50 problems, change task lengths

36. JSSP Performance (10x10)

37. JSSP Performance (15x15)

38. Summary
Case Injected Genetic AlgoRithm: A hybrid system that combines genetic algorithms with a case-based memory
Defined problem-similarity and solution-similarity metrics
Defined performance metrics and showed empirically that CIGAR learns to increase performance for sequences of similar problems

39. Conclusions
Case Injected Genetic AlgoRithm is a viable system for increasing performance with experience
Implications for system design
Increases performance with experience
Generates cases during problem solving
Long term navigable store of expertise
Design analysis by analyzing case-base