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Population Based Incremental Learning

Population Based Incremental Learning. Shumeet Baluja Presented by KC Tsui. Background. Populations based search, such as GA Create a probability matrix by counting the number of 1s and 0s in each gene position Generate new population using the probability matrix

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Population Based Incremental Learning

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  1. Population Based Incremental Learning Shumeet Baluja Presented by KC Tsui

  2. Background • Populations based search, such as GA • Create a probability matrix by counting the number of 1s and 0s in each gene position • Generate new population using the probability matrix • No information is carried from generation to generation! • Supervised Competitive learning, e.g. LVQ • Winner-take-all reinforcement learning in ANN • Winner is a kind of prototype of the sample presented • PBIL = GA + CL • Capture the trend from the best performer

  3. Basic PBIL • P  initialize probability vector (each position = 0.5) • while (generations++ < limit) • for each vector i do • for each position j do • generate Vi(j) according to P(j) • end-do • evaluate f(Vi) • end-do • Vmax = max(f(Vi)) • update P according to Vmax • if random(0,1] < Pmutate • mutate P • end-if • end-while

  4. Update and Mutation Rules • Update Rule • Mutation Rule • Pmutate = 0.02 •  = 0.05

  5. Extensions • Learning from M top scorer: Adapt the probability matrix • equally according to the M vectors, or • where there is consensus in, or most, of the M vectors • according to the rank of the M top vectors • Learning also from negative sample • move away the worst vector • modify only those positions where the best and the worst disagree

  6. Some applications • Function optimization • Job-shop scheduling • TSP • Bin-packing • Knapsack Problem • Neural Network weight training

  7. Some Comparison

  8. References • Shumeet Baluja, Population-Based Incremental Learning: a method for integrating genetic search based function optimization and competitive learning, Technical report CMU-CS-94-163. 1994. • Shumeet Baluja & Rich Caruana, Removing the Genetics from the Standard Genetic Algorithm, ICML’95. • Shumeet Baluja, An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics, Technical Report CMU-Cs-95-193, 1995.

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