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Learning from Experience: Case Injected Genetic Algorithm Design of Combinational Logic Circuits

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. Outline. Motivation

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Learning from Experience: Case Injected Genetic Algorithm Design of Combinational Logic Circuits

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  1. 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

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

  3. 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 • Seed the GA’s population

  4. Case-Based Reasoning • When confronted by a new problem, adapt similar (already solved) problem’s solution to solve new problem • Many problems in design are suited to a case-based representation • CBR  Associative Memory + Adaptation • Indexing (similarity) and adaptation are domain dependent

  5. Case Injected Genetic AlgoRithm • Combine genetic “adaptive” search with case-based memory • Case-base provides memory • Genetic algorithm provides adaptation • Genetic algorithm generates cases • A member of the GA’s population is a case

  6. System

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

  8. Combinational Logic Design • Configuration design • 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

  9. Encoding

  10. Encoding

  11. Parity

  12. 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 • …

  13. Problem similarity

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

  15. OSSP Performance

  16. Solution Similarity

  17. 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

  18. Setup • 50, 6-bit combinational logic design problems • Randomly select and flip 10% bits in parity output to define logic function • Compare performance • Quality of final design solution (correct output) • Time to this final solution (in generations)

  19. 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

  20. Problem distribution

  21. Performance - Quality

  22. Performance - Time

  23. Injection Strategies

  24. Solution distribution

  25. Summary • Case Injected Genetic AlgoRithm: A hybrid system that combines genetic algorithms with a case-based memory • Defined problem and solution similarity metrics • Defined performance metrics and empirically showed that CIGAR learns to increase performance with experience for a sequence of problems in combinational logic design • Empirically compared performance of injection strategies

  26. Conclusions • Case Injected Genetic AlgoRithm is a viable system for increasing performance with experience. • Improving one or both of • Quality of solution found – highest fitness individual • Number of generations needed to find this solution • Repeated injection based on similarity • Syntactic similarity measures suffice • Hamming distance • Longest Common Sub-string for permutation encoding

  27. Conclusions • Case Injected Genetic AlgoRithm can increase performance with experience • Implications for design systems • Performance improvement with experience • Generates cases during problem solving • Long term navigable store of expertise • Design analysis by analyzing case-base

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