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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 - PowerPoint PPT Presentation
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Learning from Experience: Case Injected Genetic Algorithm Design of Combinational Logic Circuits
Sushil J. LouisGenetic Algorithm Systems Lab (gaslab)
University of Nevada, Reno
http://www.cs.unr.edu/~sushil
http://gaslab.cs.unr.edu/
sushil@cs.unr.edu
http://gaslab.cs.unr.edu
Outline
Motivation What is the technique?
Genetic Algorithm and Case-Based Reasoning
Is it useful? Evaluate performance on Combinational Logic Design
Results Conclusions
http://gaslab.cs.unr.edu
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
http://gaslab.cs.unr.edu
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
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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
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System
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Related work
Seeding:Koza, Greffensttette, Ramsey, Louis Lifelong learning: Thrun Key Differences
Store and reuse intermediate solutions Solve sequences of similar problems
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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
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Encoding
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Encoding
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Parity
Input 3-bit Parity 3-1 problem000 0 0
001 1 0
010 1 1
011 0 0
100 1 1
101 0 0
110 0 0
111 1 1
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Which cases to inject?
Problem distance metric (Louis ‘97) Domain dependent
Solution distance metric Genetic algorithm encodings
Binary – hamming distanceReal – euclidean distancePermutation – longest common substring…
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Problem similarity
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Lessons
Storing and Injecting solutions may not improve solution quality
Storing and Injecting partial solutions does lead to improved quality
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OSSP Performance
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Solution Similarity
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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
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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)
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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
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Problem distribution
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Performance - Quality
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Performance - Time
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Injection Strategies
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Solution distribution
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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
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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
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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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