Case Injected Genetic Algorithms

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

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

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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

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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

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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

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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

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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

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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

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

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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 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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Strike force asset allocation

Allocate platforms to targets

Dynamic Changing Priority Battlefield conditions Popup Weather …

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Factors in allocation

Pilot proficiency Asset suitability Priority Risk

Route Other assets (SEAD) Weather

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Maximize mission success

Binary encoding Platform to multiple targets Target can have multiple platforms Dynamic battle-space

Strong time constraints

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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

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Results

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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

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TSP performance

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Scheduling

Job shop scheduling problems Permutation encoding (Fang) Similarity metric: Longest common

subsequence (Cormen et al, Introduction to Algorithms)

50 problems, change task lengths

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JSSP Performance (10x10)

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JSSP Performance (15x15)

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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

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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