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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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**CAP6938Neuroevolution and Developmental EncodingApproaches**toNeuroevolution Dr. Kenneth Stanley September 20, 2006**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?**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**Parallel Distributed Genetic Programming (PDGP)Pujol and**Poli (1997) • “Dual representation”: linear and graph**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)**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”**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”**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**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**ESP defeats CE**Hidden Nodes Inputs (Gomez and Miikkulainen 1999)**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?**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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