1. CAP6938 Neuroevolution and Artificial Embryogeny Approaches to Neuroevolution Dr. Kenneth Stanley February 1, 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 Encoding Gruau (1993, 1996) Indirect encoding (Artificial Embryogeny) 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 Encoding Gruau (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 (Gomez and Miikkulainen 1999) Hidden Nodes Inputs

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 Homework due 2/6/05: 1 page project proposal including project description and goals, a falsifiable hypothesis on what you expect to happen, why it involves structure, and what platform you will use (language and OS). If partners, describe briefly division of labor. Evolving Neural Networks Through Augmenting TopologiesEvolving Neural Networks Through Augmenting Topologies by Kenneth O. Stanley and Risto Miikkulainen (2002)