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CAP6938 Neuroevolution and Developmental Encoding Approaches to Neuroevolution

CAP6938 Neuroevolution and Developmental Encoding Approaches to Neuroevolution. Dr. Kenneth Stanley September 20, 2006. Many TWEANN Problems. Competing conventions problem Topology matching problem Initial population topology randomization Defective starter genomes

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CAP6938 Neuroevolution and Developmental Encoding Approaches to Neuroevolution

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  1. CAP6938Neuroevolution and Developmental EncodingApproaches toNeuroevolution Dr. Kenneth Stanley September 20, 2006

  2. 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 can’t compete in the short run • Need to protect innovation • How do researchers design NE methods?

  3. 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

  4. Parallel Distributed Genetic Programming (PDGP)Pujol and Poli (1997) • “Dual representation”: linear and graph

  5. 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)

  6. 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”

  7. 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”

  8. 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

  9. Cellular EncodingGruau (1993,1996)

  10. 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

  11. ESP defeats CE Hidden Nodes Inputs (Gomez and Miikkulainen 1999)

  12. 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?

  13. Next Class: NeuroEvolution of Augmenting Topologies (NEAT) • Directly address TWEANN challenges • Turns topology into an advantage • Applicable outside NN’s • Basis of class projects Evolving Neural Networks Through Augmenting Topologies by Kenneth O. Stanley and Risto Miikkulainen (2002)

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