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 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

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