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Evolving Neural Networks Through Augmenting Topologies

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Daniele Loiacono
Daniele Loiacono
Technology
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Evolving Neural Networks through Augmenting Topologies Authors Kenneth O. Stanley Risto Miikkulainen Speaker Daniele Loiacono
Outline ,[object Object],[object Object],[object Object],[object Object]
GA
Outline ,[object Object],[object Object],[object Object],[object Object]
The population David Brogan, 2006
Ranking by fitness David Brogan, 2006
Mate Selection Selection probability is proportional to fitness David Brogan, 2006
Crossover Exploit goodness of parents David Brogan, 2006
Mutation Explore unknown David Brogan, 2006
Best solution David Brogan, 2006
David Brogan, 2006
NN
Outline ,[object Object],[object Object],[object Object],[object Object]
Neural Networks ,[object Object],Input Output ?
Neural Networks ,[object Object],[object Object],[object Object],[object Object],Input Output
Learning weights of NNs ,[object Object],[object Object],[object Object],[object Object],[object Object]
Design of network topology ,[object Object],[object Object],[object Object],? ?
NE and TWEANN
Outline ,[object Object],[object Object],[object Object],[object Object]
Neuroevolution (NE) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TWEANN issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TWEANN Encoding ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mating in TWEANN ,[object Object],[object Object],X n1 ↔ n’1 n2 ↔ n’2 n3 ↔ n’3 n1 n2 n3 n’1 n’2 n’3
Mating in TWEANN ,[object Object],[object Object],Competing Conventions Problem … A B C A B C A C
Mating in TWEANN ,[object Object],[object Object],The Holy Grail (Radcliffe, 1993)
Protecting innovations ,[object Object],[object Object],[object Object]
Initialization and topology minimization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Structured Genetic Algorithm (sGA) Dasgupta and McGregor (1992) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Breeder Genetic Programming (Zhang and Muhlenbein) ,[object Object],[object Object],[object Object],[object Object]
Parallel Distributed Genetic Programming (PDGP) Pujol and Poli (1997) ,[object Object],[object Object],[object Object],[object Object]
GeNeralized Acquisition of Recurrent Links (GNARL) Angeline, Saunders, and Pollack (1993) ,[object Object],[object Object]
Cellular Encoding Gruau (1993, 1996) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Enforced SubPopulations (ESP) Gomez and Miikkulainen (1997,1999) ,[object Object],[object Object],[object Object]
NEAT
Outline ,[object Object],[object Object],[object Object],[object Object]
NEAT ,[object Object],[object Object],[object Object],[object Object]
Genetic Encoding in NEAT
Topological Innovation
Link Weights Mutation ,[object Object],[object Object]
Topology Matching Problem ,[object Object],[object Object],[object Object]
Historical Markings ,[object Object]
Artificial Synapsis in NEAT
Speciation in NEAT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Network similarity in NEAT ,[object Object],[object Object]
Fitness Sharing in NEAT ,[object Object],[object Object],[object Object],[object Object],[object Object]
Complexification ,[object Object],[object Object],[object Object],[object Object],[object Object]
Validation of NEAT ,[object Object],[object Object],[object Object]
APPS
Applications ,[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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