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

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case injected genetic algorithms

Case Injected Genetic Algorithms

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

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
outline1
Outline
  • Motivation
  • What is the technique?
    • Genetic Algorithm and Case-Based Reasoning
  • Is it useful?
    • Combinational Logic Design
    • Strike Force Asset Allocation
    • TSP
    • Scheduling
  • Conclusions
genetic algorithm
Genetic Algorithm
  • Non-Deterministic, Parallel, Search
  • Poorly understood problems
  • Evaluate, Select, Recombine
  • Population search
    • Population member encodes candidate solution
    • Building blocks combine to make progress
    • More resistant to local optima
    • Iterative, requiring many evaluations
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
case based reasoning
Case-Based Reasoning
  • When confronted by a new problem, adapt similar (already solved) problem’s solution to solve new problem
  • CBR  Associative Memory + Adaptation
  • CBR: Indexing (on problem similarity) and adaptation are domain dependent
case injected genetic algorithm
Case Injected Genetic AlgoRithm
  • Combine genetic search with case-based reasoning
  • Case-base provides memory
  • Genetic algorithm provides adaptation
  • Genetic algorithm generates cases
    • Any 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
  • An example of configuration 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 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

strike force asset allocation
Strike force asset allocation
  • Allocate platforms to targets
  • Dynamic
    • Changing Priority
    • Battlefield conditions
    • Popup
    • Weather
factors in allocation
Factors in allocation
  • Pilot proficiency
  • Asset suitability
  • Priority
  • Risk
    • Route
    • Other assets (SEAD)
    • Weather
maximize mission success
Maximize mission success
  • Binary encoding
  • Platform to multiple targets
  • Target can have multiple platforms
  • Dynamic battle-space
    • Strong time constraints
setup1
Setup
  • 50 problems.
  • 10 platforms, 40 assets, 10 targets
  • Each platform could be allocated to two targets
  • Problems varied in risk matrix
  • Popsize=80, Generations=80, Pc=1.0, Pm=0.05, probabilistic closest to best, injection period=9, injection % = 10% of popsize
slide33
TSP
  • Find the shortest route that visits every city exactly once (except for start city)
  • Permutation encoding. Ex: 35412
  • Similarity metric: Longest common subsequence (Cormen et al, Introduction to Algorithms)
  • 50 problems, move city locations
scheduling
Scheduling
  • Job shop scheduling problems
  • Permutation encoding (Fang)
  • Similarity metric: Longest common subsequence (Cormen et al, Introduction to Algorithms)
  • 50 problems, change task lengths
summary
Summary
  • Case Injected Genetic AlgoRithm: A hybrid system that combines genetic algorithms with a case-based memory
  • Defined problem-similarity and solution-similarity metrics
  • Defined performance metrics and showed empirically that CIGAR learns to increase performance for sequences of similar problems
conclusions
Conclusions
  • Case Injected Genetic AlgoRithm is a viable system for increasing performance with experience
  • Implications for system design
    • Increases performance with experience
    • Generates cases during problem solving
    • Long term navigable store of expertise
    • Design analysis by analyzing case-base