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BOA (Bayesian Optimization Algorithm). for Dummies. Hsuan Lee. References. Martin Pelikan: Hierarchical Bayesian Optimization Algorithm , StudFuzz 170 , 31–48 (2005 ) //BOA

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References
References

  • Martin Pelikan: Hierarchical Bayesian Optimization Algorithm, StudFuzz170, 31–48 (2005) //BOA

  • Martin Pelikan and D. E. Goldberg: Hierarchical Bayesian Optimization Algorithm, Studies in Computational Intelligence (SCI) 33, 63-90 (2006) //hBOA

  • Cooper, G. F. and Herskovits, E. H. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–347.

  • Heckerman, D., Geiger, D., and Chickering, D. M. (1994). Learning Bayesian networks: The combination of knowledge and statistical data. Technical Report MSR-TR-94-09, Microsoft Research, Redmond, WA.

  • Friedman, N., and Goldszmidt, M. (1999). Learning Bayesian networks with local structure. In Jordan, M. I., (Ed.), Graphical models, pp. 421–459. MIT, Cambridge, MA

Hsuan Lee @ NTUEE


Generating offspring
Generating Offspring

EDA

Mutate

Crossover

  • Group Reproduction

    • Use a GROUP of fit chromosome to build a model. Sample the model to generate an offspring

    • Eg. DSMGA(?) for SpinGlass, BOA

  • Asexual Reproduction

    • Use ONE fit chromosome. Change slightly to form an offspring

    • Eg. ES

  • Sexual Reproduction

    • Use a PAIR of fit chromosome. Take parts of each to form an offspring

    • Eg. sGA, DSMGA

Hsuan Lee @ NTUEE


Bayesian optimization algorithm
Bayesian Optimization Algorithm

  • Pseudo Code

    Bayesian Optimization Algorithm (BOA)

    t  0;

    generate initial population P(0);

    while (not done) {

    SELECT population of promising solution S(t);

    BUILD Bayesian network (BN) B(t) from S(t);

    SAMPLE B(t) to generate O(t);

    incorporate O(t) into P(t); //REPLACEMENT

    t t+1;

    }

Hsuan Lee @ NTUEE


Bayesian optimization algorithm1
Bayesian Optimization Algorithm

  • Initialization

Until Termination

Hsuan Lee @ NTUEE


Learning bayesian network
Learning Bayesian Network

  • Bayesian Network

    • A BN is a directed acyclic graph (DAG)

    • An edge on Bayesian Network ABimplies that the occurrence of A has an effect on the probability of B’s occurrence. A is a parent of B. B is conditionally dependent on A.

    • Two nodes are assumed to be conditionally independent if there is not an edge between them

Hsuan Lee @ NTUEE


Learning bayesian network1
Learning Bayesian Network

  • Bayesian Network

Sprinkler

Rain

Wet Grass

Hsuan Lee @ NTUEE


Learning bayesian network2
Learning Bayesian Network

  • Learning Bayesian Network from data

    • Structure (B)

      To learn the structure of a BN, we need

      • A scoring metric (or a set of scoring metrics) on structures

      • A search procedure

    • Parameters (Θ,θ)

      • Given the structure of a BN, learning parameters is straight forward.

      • Maximum Likelihood (ML),

        Learning parameters is easy,

        but learning the best BN structure is NP-Complete

Hsuan Lee @ NTUEE


Learning bayesian network3
Learning Bayesian Network

  • Scoring Metrics: evaluations of a BN structure

    • Bayesian Metrics

      Determines the likelihood of a structure given the observed data and some prior knowledge

      Eg. Bayesian Dirichlet Metric (BD)

    • Minimum Description Length Metrics

      Evaluate the structure according to the number of bits required to store the model and the data compressed according to the model

      Eg. Bayesian Information Criterion

      We’ll come back to the scoring metrics later.

Hsuan Lee @ NTUEE


Learning bayesian network4
Learning Bayesian Network

  • The Search Procedure of a good Bayesian Network

    • It can be shown that finding the best Bayesian network isNP-Complete. But the best BN is not required in BOA, a good BN is enough.

    • Greedy Algorithm can be used to find a good BN

      Greedy Algorithm of network construction

      initialize the network B (an empty network or the network of the last generation)

      done  false;

      while (not done) {

      O all simple graph operations applicable to B;

      IF there exists an operation in O that improves score(B) THEN

      op = operation from O that improves score(B) the most;

      apply op to B;

      ELSE

      done  true;

      }

      return B;

Hsuan Lee @ NTUEE


Learning bayesian network5
Learning Bayesian Network

  • Simple Graph Operations of Bayesian Network

    • Edge Addition

    • Edge Removal

    • Edge Reversal

Wet Road

Rain

Car Crash

Radar

Speed

Hsuan Lee @ NTUEE


Learning bayesian network6
Learning Bayesian Network

  • Learning Parameters

    Maximum Likelihood (ML)

Wet Road

Rain

Car Crash

Radar

Speed

Hsuan Lee @ NTUEE


Sampling bayesian network
Sampling Bayesian Network

  • Generate Offspring with a Bayesian Network

    • Given a Bayesian network with structure & parameters

    • Perform a topology sort on the Bayesian network, which is a directed acyclic graph (DAG)

    • Assign values to the new chromosome bit by bit in the topological sorted order. according to the parameters.

Wet Road

Rain

Car Crash

Radar

Speed

Hsuan Lee @ NTUEE


Bayesian optimization algorithm2
Bayesian Optimization Algorithm

  • Initialization

Until Termination

Hsuan Lee @ NTUEE


Scoring metrics revisited
Scoring Metrics Revisited

  • Minimum Description Length Metrics

    Evaluate the structure according to the number of bits required to store the model and the data compressed according to the model

    Bayesian Information Criterion

    B: Bayesian Structure

    H(A|B): Conditional Entropy of A given B

    N: population size

Hsuan Lee @ NTUEE


Scoring metrics revisited1
Scoring Metrics Revisited

  • Bayesian Metrics

    Determines the likelihood of a structure given the observed data and some prior knowledge

    Bayesian Dirichlet Metric (BD)

    B: Bayesian Structure

    D: Observed Data

    𝜉: Prior Information

    Nijk: # of Observed Data that has value k on bit i with the parent string j

    N’ijk: prior knowledge

    Γ: Gamma Function

Hsuan Lee @ NTUEE


Scoring metrics revisited2
Scoring Metrics Revisited

  • Bayesian Dirichlet Metric (BD)

    In BOA, is set to 1 and .

    This reduced form of BD metric is called K2 metric.

    Physical meaning: all outcomes k of a given parental setup has the same probability at the beginning .

    The term can be set either to a constant or set to favor simpler structures.

Hsuan Lee @ NTUEE


Scoring metrics revisited3
Scoring Metrics Revisited

  • Decomposability of scoring metrics

    • In both metrics, the score of a structure only changes locally after performing a simple graph operation (by greedy search)

    • Only one particular term (one particular i) is changed in the entire metric

      Largely simplifies the computation of the greedy search

Hsuan Lee @ NTUEE


Scoring metrics revisited4
Scoring Metrics Revisited

  • Problems exist in both scoring metrics

    • In BIC, the term about model complexity confines the complexity of the Bayesian structure, resulting in over simplified structures

    • In BD, maximizing marginal probability leads to over-fitting, resulting in over complicated structures

      A combination of both can produce favorable results

Hsuan Lee @ NTUEE


Boa bayesian optimization algorithm
hBOA

Hierarchical Bayesian Optimization Algorithm


Hierarchical boa hboa
Hierarchical BOA (hBOA)

  • The hierarchical version of BOA, used to solve nearly decomposable and hierarchical problems

  • Three important challenges must be considered for the design of solvers of difficult hierarchical problems

    • Decomposition

      Bayesian Network

    • Chunking

      Representing partial solutions at each level compactly to enable the algorithm to effectively process partial solutions for higher order.

      Using local structures

    • Diversity Maintenance

      RTR replacement

Hsuan Lee @ NTUEE


Hierarchical boa hboa1
Hierarchical BOA (hBOA)

  • Local Structure

    Decision Tree, in hBOA

  • Full Table

Hsuan Lee @ NTUEE


Hierarchical boa hboa2
Hierarchical BOA (hBOA)

  • Benefits of building local structure

    • Simplifies the model

      In the case shown, 8 parameters has to be maintained for full conditional probability model table, but only 4 for decision tree

    • Generalizes the parental condition

      In the case shown, with the full table setting, an occurrence of ABCX=1010 contributes nothing in predicting ABCX=1110 in the future; with the local structure 1010 DOES predict 1110

Hsuan Lee @ NTUEE


Hierarchical boa hboa edit
Hierarchical BOA (hBOA) //EDIT

  • Scoring Metrics: evaluations of a local structure Bi

    • Bayesian Metrics

      In hBOA, is set to favor simpler models.

Hsuan Lee @ NTUEE


Hierarchical boa hboa edit1
Hierarchical BOA (hBOA) //EDIT

  • Scoring Metrics: evaluations of a local structure Bi

    • Minimum Description Length Metrics

Hsuan Lee @ NTUEE


Hierarchical boa hboa3
Hierarchical BOA (hBOA)

  • Search procedure for local structure (decision tree)

    Greedy Algorithm of local structure (decision tree) construction

    initialize the structure Bi (a one-node tree that represents all parental strings)

    // top-down

    Branch (Bi , Πi);

    return Bi;

    Branch (T, P)

    IF exists elements in P THEN

    choose π∈ Pthat best splits the decision tree T;

    Left Child = Branch (Tπ=1 , P- π);

    Right Child = Branch (Tπ=0 , P- π);

    // bottom-up

    IF the score given by Tπ=1 and Tπ=0is worse than T THEN

    merge Tπ=1 and Tπ=0 back into T;

    ELSE

    Left Child = Right Child = NIL;

    return T;

Hsuan Lee @ NTUEE


Hierarchical boa hboa4
Hierarchical BOA (hBOA)

  • Search procedure for local structure (Decision Tree)

    demonstration

  • A=1

  • A=0

  • C=1

  • C=0

  • B=1

  • B=0

  • B=1

  • B=0

  • B=1

  • B=0

  • C=1

  • C=0

  • C=1

  • C=0

Hsuan Lee @ NTUEE


Hierarchical boa hboa5
Hierarchical BOA (hBOA)

  • Modified network construction for hBOA

    Greedy Algorithm of network with local structure construction

    initialize the network B (an empty network or the network of the last generation)

    done  false;

    while (not done) {

    O  all simple graph operations applicable to B;

    optimize every structure in O with local structure;

    IF there exists an operation in O that improves score(B) THEN

    op = operation from O that improves score(B) the most;

    apply op to B;

    ELSE

    done  true;

    }

    return B;

Hsuan Lee @ NTUEE


Hierarchical boa hboa6
Hierarchical BOA (hBOA)

  • Sampling a Bayesian network with local structure

    • Topology sort

    • Assign values according to local structures, instead of full conditional probability tables

Wet Road

Rain

Car Crash

Radar

Speed

Hsuan Lee @ NTUEE


Some thoughts about boa hboa
Some Thoughts about BOA/hBOA

  • Use causal Bayesian network to solve an acausal problem

    • Are arrows really needed?

    • “Markovian” Optimization Algorithm, MOA?

    • Adopt the idea of Bayesian Dirichlet Metric.

Wet road

Rain

Car Crash

Radar

Speed

Hsuan Lee @ NTUEE


End of presentation thank you thank yu
End of PresentationThank You! Thank Yu!