Case Injected Genetic Algorithms Sushil J Louis Genetic

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

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/ [email protected] unr. edu

Outline w Motivation w What is the technique? n Genetic Algorithm and Case-Based Reasoning

Outline w Motivation w What is the technique? n Genetic Algorithm and Case-Based Reasoning w Is it useful? n Evaluate performance on Combinational Logic Design w Results w Conclusions http: //gaslab. cs. unr. edu

Outline w Motivation w What is the technique? n Genetic Algorithm and Case-Based Reasoning

Outline w Motivation w What is the technique? n Genetic Algorithm and Case-Based Reasoning w Is it useful? n n Combinational Logic Design Strike Force Asset Allocation TSP Scheduling w Conclusions http: //gaslab. cs. unr. edu

Genetic Algorithm w Non-Deterministic, Parallel, Search w Poorly understood problems w Evaluate, Select, Recombine

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

Motivation w Deployed systems are expected to confront and solve many problems over their

Motivation w Deployed systems are expected to confront and solve many problems over their lifetime w How can we increase genetic algorithm performance with experience? w Provide GA with a memory http: //gaslab. cs. unr. edu

Case-Based Reasoning w When confronted by a new problem, adapt similar (already solved) problem’s

Case-Based Reasoning w When confronted by a new problem, adapt similar (already solved) problem’s solution to solve new problem w CBR Associative Memory + Adaptation w CBR: Indexing (on problem similarity) and adaptation are domain dependent http: //gaslab. cs. unr. edu

Case Injected Genetic Algo. Rithm w Combine genetic search with case-based reasoning w Case-base

Case Injected Genetic Algo. Rithm w Combine genetic search with case-based reasoning w Case-base provides memory w Genetic algorithm provides adaptation w Genetic algorithm generates cases n Any member of the GA’s population is a case http: //gaslab. cs. unr. edu

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

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

Related work w Seeding: Koza, Greffensttette, Ramsey, Louis w Lifelong learning: Thrun w Key

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

Combinational Logic Design w An example of configuration design w Given a function and

Combinational Logic Design w An example of configuration design w Given a function and a target technology to work with design an artifact that performs this function subject to constraints n n n Target technology: Logic gates Function: Parity checking Constraints: 2 -D gate array http: //gaslab. cs. unr. edu

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

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

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

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

Parity Input 3 -bit Parity 3 -1 problem 000 001 010 011 100 101

Parity Input 3 -bit Parity 3 -1 problem 000 001 010 011 100 101 110 111 0 1 0 0 1 http: //gaslab. cs. unr. edu

Which cases to inject? w Problem distance metric (Louis ‘ 97) n Domain dependent

Which cases to inject? w Problem distance metric (Louis ‘ 97) n Domain dependent w Solution distance metric n Genetic algorithm encodings Binary – hamming distance l Real – euclidean distance l Permutation – longest common substring l… l http: //gaslab. cs. unr. edu

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

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

Solving parity problem after solving parity-8 w Inject solution to parity 6 -8 into

Solving parity problem after solving parity-8 w Inject solution to parity 6 -8 into population of ga solving parity 6 n Storing and Injecting solutions may not improve solution quality w Inject a solution from a prior generation of GA solving parity-8 n Storing and Injecting partial solutions does lead to improved quality http: //gaslab. cs. unr. edu

Injecting earlier (partial) solutions on OSSP http: //gaslab. cs. unr. edu

Injecting earlier (partial) solutions on OSSP http: //gaslab. cs. unr. edu

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

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

Periodic Injection Strategies w Closest to best w Furthest from worst w Probabilistic closest

Periodic Injection Strategies w Closest to best w Furthest from worst w Probabilistic closest to best w Probabilistic furthest from worst w Randomly choose a case from case-base w Create random individual http: //gaslab. cs. unr. edu

Setup w Generate 50 different test problems that are similar n 6 -bit combinational

Setup w Generate 50 different test problems that are similar n 6 -bit combinational logic design problems w Randomly select and flip bits in parity output to define logic function w Compare performance n n Quality of final design solution (correct output) Time to this final solution (in generations) http: //gaslab. cs. unr. edu

Number of problems solved Time needed w Achieve better fitness achieved as you solve

Number of problems solved Time needed w Achieve better fitness achieved as you solve more problems w OR w Need less time to achieve better fitness as you solve more problems Fitness A good outcome would Number of problems solved http: //gaslab. cs. unr. edu

Parameters w w w Population size: 30 No of generations: 30 CHC (elitist) selection

Parameters w w w Population size: 30 No of generations: 30 CHC (elitist) selection Scaling factor: 1. 05 Prob. Crossover: 0. 95 Prob. Mutation: 0. 05 w Store best individual every generation w Inject every 5 generations (2^5 = 32) w Inject 3 cases (10%) w Multiple injection strategies Averages over 10 runs http: //gaslab. cs. unr. edu

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

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

Performance – Quality (avg over 30 runs) http: //gaslab. cs. unr. edu

Performance – Quality (avg over 30 runs) http: //gaslab. cs. unr. edu

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

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

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

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

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

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

Strike force asset allocation w Allocate platforms to targets w Dynamic n n n

Strike force asset allocation w Allocate platforms to targets w Dynamic n n n Changing Priority Battlefield conditions Popup Weather … http: //gaslab. cs. unr. edu

Factors in allocation w Pilot proficiency w Asset suitability w Priority w Risk n

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

Maximize mission success w Binary encoding w Platform to multiple targets w Target can

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

Setup w w w 50 problems. 10 platforms, 40 assets, 10 targets Each platform

Setup w w w 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 http: //gaslab. cs. unr. edu

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

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

TSP w Find the shortest route that visits every city exactly once (except for

TSP w Find the shortest route that visits every city exactly once (except for start city) w Permutation encoding. Ex: 35412 w Similarity metric: Longest common subsequence (Cormen et al, Introduction to Algorithms) w 50 problems, move city locations http: //gaslab. cs. unr. edu

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

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

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

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

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

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

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

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

Summary w Case Injected Genetic Algo. Rithm: A hybrid system that combines genetic algorithms

Summary w Case Injected Genetic Algo. Rithm: A hybrid system that combines genetic algorithms with a case-based memory w Defined problem-similarity and solutionsimilarity metrics w Defined performance metrics and showed empirically that CIGAR learns to increase performance for sequences of similar problems http: //gaslab. cs. unr. edu

Conclusions w Case Injected Genetic Algo. Rithm is a viable system for increasing performance

Conclusions w Case Injected Genetic Algo. Rithm is a viable system for increasing performance with experience w Implications for system design n n Increases performance with experience Generates cases during problem solving Long term navigable store of expertise Design analysis by analyzing case-base http: //gaslab. cs. unr. edu