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Transcript
```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
Review NNs
• Learning
– supervised (backprop)
– unsupervised (competitive learning, selforganizing networks)
• Examples
– NETtalk: Backprop learning of pronunciation;
input is text (windows); output is articulatory
– 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
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, ‘Life’s really Big
Questions, Dec 2000) featuring Karl Sims and
Jordan Pollack
GA
Evolutionary Algorithms - Principles
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).
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.
Evolutionary Algorithm - Structure
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
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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