Download Slide 1

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Microevolution wikipedia, lookup

Gene expression programming wikipedia, lookup

Population genetics wikipedia, lookup

Koinophilia wikipedia, lookup

Group selection wikipedia, lookup

Dual inheritance theory wikipedia, lookup

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
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
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
Evolutionary Algorithms - Principles
Evolution Processes I
• Selection determines, which individuals are
chosen for mating (recombination) and how
many offspring each selected individual
• 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
• 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
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