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Case Injected Genetic Algorithms Sushil J. Louis Genetic Algorithm Systems Lab (gaslab) University of Nevada, Reno

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1 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

2 http://gaslab.cs.unr.edu Collaborators  Vinod Gandikota  Igor Golovkin  Xiaohua Liu  Andrew Murray  Rikun Tang  Indira Vinjamuri  Yongmian Zhang

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

4 http://gaslab.cs.unr.edu Genetic Algorithm  Non-Deterministic, Parallel, Search  Poorly understood problems  Evaluate, Select, Recombine  Population based search Population member encodes candidate solution Building blocks combine to make progress More resistant to local optima Iterative, requiring many evaluations

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

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

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

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

9 http://gaslab.cs.unr.edu Related work  Seeding:Koza, Greffensttette, Ramsey, Louis  Lifelong learning: Thrun  Key Differences Store and reuse intermediate solutions Solve sequences of similar problems

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

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

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

13 http://gaslab.cs.unr.edu Parity Input3-bit Parity3-1 problem 00000 00110 01011 01100 10011 10100 11000 11111

14 http://gaslab.cs.unr.edu Problem similarity

15 http://gaslab.cs.unr.edu Lessons  Storing and Injecting solutions may not improve solution quality  Storing and Injecting partial solutions does lead to improved quality

16 http://gaslab.cs.unr.edu OSSP Performance

17 http://gaslab.cs.unr.edu 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 …

18 http://gaslab.cs.unr.edu Solution Similarity

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

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

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

22 http://gaslab.cs.unr.edu Problem distribution

23 http://gaslab.cs.unr.edu Performance - Quality

24 http://gaslab.cs.unr.edu Performance - Time

25 http://gaslab.cs.unr.edu Injection Strategies

26 http://gaslab.cs.unr.edu Solution distribution

27 http://gaslab.cs.unr.edu Strike force asset allocation  Allocate platforms to targets  Dynamic Changing target priority Battlefield conditions Popup Weather …

28 http://gaslab.cs.unr.edu Factors in allocation  Pilot proficiency  Asset suitability  Priority  Risk Route Other assets (SEAD) Weather

29 http://gaslab.cs.unr.edu Maximize mission success  Binary encoding  Platform to multiple targets  Target can have multiple platforms  Dynamic battle-space Strong time constraints

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

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

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

33 http://gaslab.cs.unr.edu TSP performance

34 http://gaslab.cs.unr.edu Scheduling  Job shop scheduling problems  Permutation encoding (Fang)  Similarity metric: Longest common subsequence (Cormen et al, Introduction to Algorithms)  50 problems, change task lengths

35 http://gaslab.cs.unr.edu JSSP Performance (10x10)

36 http://gaslab.cs.unr.edu JSSP Performance (15x15)

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

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

39 http://gaslab.cs.unr.edu Implications  Implications for system design Increases performance with experience Generates cases during problem solving Long term navigable store of expertise Problem analysis by analyzing case-base


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