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CSSS 2008 Beijing-Readings-Week-Three: Difference between revisions

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*[[Media:Altenberg_Lecture_1_4up.pdf | Lecture 1: Introduction to Evolutionary Computation]]
*[[Media:Altenberg_Lecture_1_4up.pdf | Lecture 1: Introduction to Evolutionary Computation]]
*[http://www.santafe.edu/~altenber/CSSS_BEIJING/2008/Analysis_2008_4up.pdf Lecture 2: Mathematical Analysis of Evolutionary Algorithms] (1 MB)


Pending uploading of my 2008 lecture notes, here are the lectures from the 2007 CSSS Beijing:
Pending uploading of my 2008 lecture notes, here are the lectures from the 2007 CSSS Beijing:
*[http://www.santafe.edu/~altenber/CSSS_BEIJING/2007/Analysis_2007_new.2up.pdf Lecture 2: Mathematical Analysis of Evolutionary Algorithms] (1 MB)
*[http://www.santafe.edu/~altenber/CSSS_BEIJING/2007/Lecture_3.4up.pdf Lecture 3: Higher Order Evolutionary Phenomena] (9MB)
*[http://www.santafe.edu/~altenber/CSSS_BEIJING/2007/Lecture_3.4up.pdf Lecture 3: Higher Order Evolutionary Phenomena] (9MB)



Revision as of 10:16, 14 July 2008

CSSS 2008 Beijing


Lee Altenberg

Lecture Notes

Pending uploading of my 2008 lecture notes, here are the lectures from the 2007 CSSS Beijing:

Additional Reading

  • Altenberg, L. , 1994. The Schema Theorem and Price's Theorem delves into the claims about schema processing as the source of power in genetic algorithms, and recasts the Schema Theorem (Holland 1975) by using Price's Theorem (1970). It is shown that the Schema Theorem says nothing about a GA's power, but a modification with a different measurement function produces a theorem about evolvability that is a local measure of GA power. The concept of rugged landscapes is also deconstructed in terms of operator-defined distance.


(Samuel) Qing-Shan Jia

Lecture Notes

Additional Reading


Susanna Still

Chris Wiggins

Lecture Notes

  • Bayes
    • History
    • What he said
    • What he didn't say
  • Bayes rule in Dynamics
    • Random walks
      • Pollen grains
      • Wall street
    • Chemical kinetics
    • Polymer Physics
  • Bayes rule in Statistics
    • Why we fit
    • Regularization
    • Latent variables / Mixture modeling
    • Model selection the Bayesian way
    • Bayesian density estimation / unsupervised learning
  • Alternatives to Bayes
    • Cross validation
    • pvalueology
    • Information theoretic methods