CAP6938 Neuroevolution and Developmental Encoding Approaches to Neuroevolution

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CAP6938Neuroevolution and Developmental
EncodingApproaches toNeuroevolution

• Dr. Kenneth Stanley
• September 20, 2006

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Many TWEANN Problems

• Competing conventions problem
• Topology matching problem
• Initial population topology randomization
• Defective starter genomes
• Unnecessarily high-dimensional search space
• Loss of innovative structures
• More complex cant compete in the short run
• Need to protect innovation
• How do researchers design NE methods?

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Breeder Genetic Programming (Zhang and Muhlenbein)

• Represent network as a tree (TWEANN)
• Only crossover adapts topology
• Attempt to minimize both complexity and error
• Tested with parity and majority functions

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Parallel Distributed Genetic Programming
(PDGP)Pujol and Poli (1997)

• Dual representation linear and graph

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Parallel Distributed Genetic Programming
(PDGP)Pujol and Poli (1997)

• 2D genome uses overrepresentation
• Several crossover operators use properties of
  both 1D and 2D representations (e.g. subgraph
  swapping)
• Also several mutation operators
• Fixed-sized genome
• Also tested on parity (and later control)

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GeNeralized Acquisition of Recurrent Links
(GNARL)Angeline, Saunders, and Pollack (1993)

• Thus, the prospect of evolving connectionist
  networks with crossover appears limited in
  general, and better results should be expected
  with reproduction heuristics that respect the
  uniqueness of the distributed representations.
• Random initial networks
• Fixed-sized genomes
• Structural mutations
• Tested with Inducing Languages and Ant
  Problem

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Structured Genetic Algorithm (sGA)Dasgupta and
McGregor (1992)

• Standard matrix representation
• Size of matrix is square of nodes
• Maximum net size for fixed matrix size
• No thought to crossover (just plain GA)
• Tested on multi-solution functions

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Cellular EncodingGruau (1993, 1996)

• Indirect encoding (Developmental)
• First method to balance 2 poles without velocity
  inputs
• Biological motivation grow from single cell
• Gruau proved CE can generate any graph
• Crossover swaps subtrees like GP
• Indirect encoding only makes competing
  conventions harder to comprehend

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Cellular EncodingGruau (1993,1996)
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Enforced SubPopulations (ESP)Gomez and
Miikkulainen (1997,1999)

• Fixed-topology successor to Symbiotic Adaptive
  NeuroEvolution (SANE Moriarty and Miikkulainen
  1996)
• Neurons evolved in subpopulations
• One subpopulation for each hidden neuron
• Cooperative coevolution
• Interesting circumvention of competing
  conventions

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ESP defeats CE
Hidden Nodes
Inputs
(Gomez and Miikkulainen 1999)
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TWEANNS need Principles

• Is there a principled method for evolving
  topologies that is not ad hoc?
• How can the TWEANN challenges be handled
  directly?
• Are all TWEANNs created equal?

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Next Class NeuroEvolution of Augmenting
Topologies (NEAT)

• Directly address TWEANN challenges
• Turns topology into an advantage
• Applicable outside NNs
• Basis of class projects

Evolving Neural Networks Through Augmenting
Topologies by Kenneth O. Stanley and Risto
Miikkulainen (2002)