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 - PowerPoint PPT Presentation

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

Sushil J. Louis

Genetic Algorithm Systems Lab (gaslab)

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

http://gaslab.cs.unr.edu/

sushil@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
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
• 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
• Combine genetic “adaptive” search with case-based memory
• Case-base provides memory
• Genetic algorithm generates cases
• A member of the GA’s population is a case
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
• 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?
• Problem distance metric (Louis ‘97)
• Domain dependent
• Solution distance metric
• Genetic algorithm encodings
• Binary – hamming distance
• Real – euclidean distance
• Permutation – longest common substring
Lessons
• Storing and Injecting solutions may not improve solution quality
• Storing and Injecting partial solutions does lead to improved quality
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
• 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)
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
• 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
• 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
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