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

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sushil@cs.unr.edu

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Outline

Motivation What is the technique?

Genetic Algorithm and Case-Based Reasoning

Is it useful? Evaluate performance on Combinational Logic Design

Results Conclusions

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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

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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