Review NNs

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Review NNs

• Processing Principles in Neuron / Unit
• integrated input sum of weighted outputs
• activation transfer (threshold, sigmoid, linear
  function new activation state output)
• NN Architectures (graph structure ...)
• feedforward
• recurrent
• completely connected
• connection graph (with weights) can be written as
  matrix

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Review NNs

• Learning
• supervised (backprop)
• unsupervised (competitive learning,
  self-organizing networks)
• Examples
• NETtalk Backprop learning of pronunciation
  input is text (windows) output is articulatory
  features weights adjusted with delta-rule
• SOM self-organizing network adjusts weight
  vector (weights on input lines) of units towards
  best fitting input units represent classes of
  similar inputs character recognition

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74.419 Artificial Intelligence 2004 -
Evolutionary Algorithms -

• Principles of Evolutionary Algorithms
• Structure of Evolutionary Algorithms
• Michel Toulouse's Slides
• Short note on Motion Control
• Demos (PBS Archives, Lifes really Big
  Questions, Dec 2000) featuring Karl Sims and
  Jordan Pollack

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GA
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Evolutionary Algorithms - Principles
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Evolution Processes I

• Selection determines, which individuals are
  chosen for mating (recombination) and how many
  offspring each selected individual produces.
• In order to determine the new population
  (generation), each individual of the current
  generation is objected to an evaluation based on
  a fitness function.
• This fitness is used for the actual selection
  step, in which the individuals producing
  offspring are chosen (mating pool).

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Evolution Process II

• Recombination produces new individuals in
  combining the information contained in the
  parents, e.g. cross-over.
• Mutations are determined by small perturbations
  of parameters describing the individuals, which
  yield new offspring individuals.
• Re-iterate Evolution Process until system
  satisfies optimization demands.

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Evolutionary Algorithm - Structure
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Motor Control

• Define system based on physical description of
  architecture, including limbs and joints
  (parameterized)
• Specify and modify parameters for control
• ? trained Neural Network Controller
• (sensor-actuator networks)
• ? Evolution of System
• (optimization criteria is movement in
  environment race with other creatures)
• ? Karl Sims, MIT Leg Lab, Jordan Pollack

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References

• Key Researchers
• John H. Holland, University of Michigan, 1975
• H.-P. Schwefel, University of Dortmund, Germany,
  1973
• Udo Rechenberg, University of Berlin, Germany,
  1975, 1981
• Karl Sims, GenArts Inc. Cambridge, MA
• http//www.genarts.com/karl/
• Figures in this presentation taken from The
  Genetic and Evolutionary Algorithm Toolbox for
  use with Matlab (GEATbx)
• www.geatbx.com/docu/algindex.html

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