Case Injected Genetic Algorithms

1. Case Injected Genetic Algorithms Sushil J. Louis Genetic Algorithm Systems Lab (gaslab) University of Nevada, Reno http://www.cs.unr.edu/~sushil http://gaslab.cs.unr.edu/ sushil@cs.unr.edu

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 http://www.cs.unr.edu/~sushil http://gaslab.cs.unr.edu/ sushil@cs.unr.edu

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

4. Outline • Motivation • What is the technique? • 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

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. 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 Parameters 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