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Evolution and Learning
Braitenberg 6: Selection, the Impersonal Engineer
Braitenberg 7: Concepts
EXAMPLES:
Modeling evolution: genetic algorithms
Modeling learning: reinforcement learning
(bee foraging)
Genetic Algorithms
genetic coding (chromosome/genes)
repeat
genotype  phenotype (development)
evaluation, of individuals in a population
selection, based on fitness
variation, genetic modification of selected
sensorL
motorL
sensorR
motorR
Parameters to encode:
Input gains:
2
Time constants:
2
Thresholds:
4
Injection current:
4
Injection noise:
4
Synaptic weights:
4
Syn. time constants: 4
Output gains:
2
TOTAL:
26
Assume 4 bit resolution per
parameter (16 possible values):
26 * 4 = 104 bits
2104 possibilities !!!
Learning vs. evolution
Functional
organization
(bee brain)
Reinforcement Learning
Montague PR, Dayan P, Person C, Sejnowski TJ (1995) Bee
foraging in uncertain environments using predictive Hebbian
learning. Nature 377, 725 - 728
Sensory
Input
Nectar
S
Y
N
B
Action
R
WY
r(t)
WN
P
WB
δ(t)
Diagram from: http://www.cs.stir.ac.uk/~kjt/teaching/31z7/posters/2003/igb.ppt