Case Injected Genetic Algorithms

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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

Sushil J. Louis

Genetic Algorithm Systems Lab (gaslab)

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

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
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
• 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
• 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
• 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
• Combine genetic search with case-based reasoning
• Case-base provides memory
• Genetic algorithm generates cases
• Any 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
• 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?
• 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 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

Strike force asset allocation
• Allocate platforms to targets
• Dynamic
• Changing Priority
• Battlefield conditions
• Popup
• Weather
Factors in allocation
• Pilot proficiency
• Asset suitability
• Priority
• Risk
• Route
• Weather
Maximize mission success
• Binary encoding
• Platform to multiple targets
• Target can have multiple platforms
• Dynamic battle-space
• Strong time constraints
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
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
• Job shop scheduling problems
• Permutation encoding (Fang)
• Similarity metric: Longest common subsequence (Cormen et al, Introduction to Algorithms)
• 50 problems, change task lengths
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
• 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