evolutionary robotics neat / hyperneat stanley, k.o., miikkulainen (2001) evolving neural networks...
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Evolutionary RoboticsNEAT / HyperNEAT
Stanley, K.O., Miikkulainen (2001) Evolving Neural Networks through Augmenting Topologies.
Competing Conventions:
Two neural networks:
Both encode thesame function;
Have differentconventions for doingso.
No matter how they’recrossed, their childrenwill lack information.
Evolutionary RoboticsNEAT / HyperNEAT
Stanley, K.O., Miikkulainen (2001) Evolving Neural Networks through Augmenting Topologies.
Genetic encoding ofneural networksin (N)euro(e)evolutionof (A)ugmenting(T)opologies: (NEAT)
Evolutionary RoboticsNEAT / HyperNEAT
Stanley, K.O., Miikkulainen (2001) Evolving Neural Networks through Augmenting Topologies.
Historical Markings:
Keep a global counter;every time a neuron orsynapse is added,assign the value of thecounter, and increment it.
Genes/synapsescan be disabled,but remain in the genome.
Evolutionary RoboticsNEAT / HyperNEAT
Stanley, K.O., Miikkulainen (2001) Evolving Neural Networks through Augmenting Topologies.
NEAT designed soThat crossover is
(1) Algorithmically simple
and
(2) Produces childrenthat are similar to theirparents.
Evolutionary RoboticsNEAT / HyperNEAT
Stanley, K.O., Miikkulainen (2001) Evolving Neural Networks through Augmenting Topologies.
Take two parentNNs:
Line up connection genesaccording to theirhistorical markings.
For matching genes,copy either gene into childat random.
Disjoint genes (thosein the middle without apartner gene)
Excess genes (those atthe end)
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
HyperNEAT:
Evolves neural networks(compositionalpattern-producingnetworks)that produce regularpatterns spatially:
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
HyperNEAT:
HyperNEAT “paints”regular patternson to a hypercube.
The dimensionalityof the hypercube isdetermined by thedimension of theinput coordinates.
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
HyperNEAT canbe usedto “paint” weightson to synapses ofa second neural network.
Requires that eachneuron and synapsehave a 3D location:
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
HyperNEAT canbe usedto “paint” weightson to synapses ofa second neural network.
…why do this,if NEAT already evolvesneural networks?
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
Compare HyperNEAT to NEAT:
FT-NEAT: Fixed Topology NEAT.
Use same NN as in HyperNEAT;allow for mutation and crossover, butnot the addition/removal of neurons/synapses.
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
Results fromevolving locomotion.
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
HyperNEAT repeatedlyfinds regular gaits;
FT-NEAT does not.
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
For mutations,
mean fitness of childrencompared to parents:
HyperNEAT: FT-NEAT:
but HyperNEAT tendsto create more fitchildren than FT-NEAT.
..why?
It also creates muchworse children, butthese are discarded.
Evolutionary RoboticsNEAT / HyperNEAT
Clune, J. et al. (2009) Evolving Coordinated
Quadruped Gaits with the HyperNEAT Generative
Encoding
If children are producedby crossover…
In HyperNEAT,offspring tend to be morelike their parents thanin FT-NEAT.
…why?