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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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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
Outline
  • Motivation
  • What is the technique?
    • Genetic Algorithm and Case-Based Reasoning
  • Is it useful?
    • Evaluate performance on Combinational Logic Design
  • Results
  • Conclusions
motivation
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
case based reasoning
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
case injected genetic algorithm
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
related work
Related work
  • Seeding:Koza, Greffensttette, Ramsey, Louis
  • Lifelong learning: Thrun
  • Key Differences
    • Store and reuse intermediate solutions
    • Solve sequences of similar problems
combinational logic design
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
which cases to inject
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
lessons
Lessons
  • Storing and Injecting solutions may not improve solution quality
  • Storing and Injecting partial solutions does lead to improved quality
periodic injection strategies
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
setup
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)
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

Parameters

Averages over 10 runs

summary
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
conclusions
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
conclusions1
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