1. 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

2. Review NNs Learning Examples 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

3. 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

8. GA

9. Evolutionary Algorithms - Principles

12. 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).

13. 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.

14. Evolutionary Algorithm - Structure

16. 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

17. 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
Figures in this presentation taken from ‘The Genetic and Evolutionary Algorithm Toolbox for use with Matlab (GEATbx)’