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The Assessment and Application of Lineage Information in Genetic Programs for Producing Better Models Gary D. Boetticher Boetticher@uhcl.edu Univ. of Houston - Clear Lake, Houston, TX, USA http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration Kim Kaminsky Kaminsky@uhcl.edu Univ. of Houston - Clear Lake, Houston, TX, USA

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About the Author: Gary D. Boetticher http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration Ph.D. in Machine Learning and Software Engineering A neural network-based software reuse economic model Executive member of IEEE Reuse Standard Committees (1990s) Commercial consultant: U.S. Olympic Committee, LDDS Worldcom, Mellon Mortgage, … Currently: Associate Professor Department of Comp. Science/Software Engineering University of Houston - Clear Lake, Houston, TX, USA boetticher@uhcl.edu Research interests: Data mining, ML, Computational Bioinformatics, and Software metrics

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Motivating Questions Does chromosome lineage information within a Genetic Program (GP) provide any insight into the effectiveness of solving problems? If so, how could these insights be utilized to make better breeding decisions? http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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2) Determine the fitness for each (1 /Stand. Error) http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration Genetic Program Overview X, Y, and Z RESULT? XYZRESULT 24530 53216 :::: 13624 1) Create a population of equations Eq#Equation 1X+Y 2(Z-X)*Y+X :: 1000(X*X)-Z 87 84 : 57 3) Breed Equations X + Y (Z-X) * Y+X (Z-X) + Y X * Y+X 4) Generate new populations and breed until a solution is found

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Genetic Program Overview EquationFitness (X+Y)87 (X - Z) * (Y * Y)86 ZYZY 75 :: Y22 Y - X18 Generation N Generation N+1 EquationFitness (X - Z) (X + Y) * (Y * Y) Z + Y : X Y + Y Why discard legacy information? http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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Goal: Examine fitness patterns over time EquationFitness (X+Y)87 (X - Z) * (Y * Y)86 ZY85 (X - Z) * (Y * Y)84 Y79 Y - X75 Z + Y75 (X - Z) * (Y * Y)75 Y73 Y - X71 (X - Z) * (Y * Y) + W + W68 Y - X67 ZY66 (X - Z) * (Y * Y)66 Y65 Y - X65 (X - Z) * (Y * Y) + W + W64 Y - X64 Z - Y62 (X - Z) * (Y * Y)59 Y58 Y - X55 (X - Z) * (Y * Y) + W + W44 EquationFitness (X+Y)87 (X - Z) * (Y * Y)86 ZY85 (X - Z) * (Y * Y)84 Y79 Y - X75 Z + Y75 (X - Z) * (Y * Y)75 Y73 Y - X71 (X - Z) * (Y * Y) + W + W68 Y - X67 ZY66 (X - Z) * (Y * Y)66 Y65 Y - X65 (X - Z) * (Y * Y) + W + W64 Y - X64 Z - Y62 (X - Z) * (Y * Y)59 Y58 Y - X55 (X - Z) * (Y * Y) + W + W44 EquationFitness (X+Y)87 (X - Z) * (Y * Y)86 ZY85 (X - Z) * (Y * Y)84 Y79 Y - X75 Z + Y75 (X - Z) * (Y * Y)75 Y73 Y - X71 (X - Z) * (Y * Y) + W + W68 Y - X67 ZY66 (X - Z) * (Y * Y)66 Y65 Y - X65 (X - Z) * (Y * Y) + W + W64 Y - X64 Z - Y62 (X - Z) * (Y * Y)59 Y58 Y - X55 (X - Z) * (Y * Y) + W + W44 http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration Generation 1 Generation 2 Generation 3 Localized? Volatile?

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Proof of Concept Experiments - 1 5 experiments using synthetic equations: Z = W + X + Y Z = 2 * X + Y – W Z = X / Y Z = X 3 Z = W 2 + W * X - Y Data slightly perturbed to prevent premature convergence Genetic Program 1000 Chromosomes (Equations) 50 Generations Breeding based on fitness rank http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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Proof of Concept Experiments - 2 For the 1000 Chromosomes: Divide into 5 groups of 200 (by fitness) Focus on the best, middle, and worst groups See where each group’s offspring occur in the next generation http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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Results for Z = W + X + Y Best Middle Worst http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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Results for Z = 2 * X + Y – W Best Middle Worst http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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Results for Z = X / Y Best Middle Worst http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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Results for Z = X 3 Best Middle Worst http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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Results for Z = W 2 + W * X - Y Best Middle Worst http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration

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Applied Experiments Best class produces best offspring. Now what? Compare 2 Genetic Programs (GPs) 1) Use a vanilla-based GP 2) Use a GP that breeds only the top 20% of a population and replicates 5 times. http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration Genetic Program 1000 Chromosomes (Equations) 50 Generations 20 Trials Equations to model Z = Sin(W) + Sin(X) + Sin(Y) Z = log 10 (W X ) + (Y * Z)

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Results for Z = Sin(W) + Sin(X) + Sin(Y) http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration Vanilla-Based GP Lineage-Based GP Average Fitness591.8740.9 Average r 2 0.87340.9315 Ave. Generations needed to complete 29.1 28.5

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Results for Z = log 10 (W X ) + (Y * Z) http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration Vanilla-Based GP Lineage-Based GP Average Fitness210.9346.5 Average r 2 0.72440.8069 Ave. Generations needed to complete 50.0 48.6

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Conclusions http://nas.cl.uh.edu/boetticher/publications.htmlThe 2006 IEEE International Conference on Information Reuse and Integration Proof of concept experiments demonstrate the viability of considering lineage in GPs Applied experiments show that lineage-based GP modeling produce better results faster

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