ANNIE EA Papers Sampling the Nature of a Population: Punctuated Anytime Learning for Co- Evolving a Team – Gary Parker, H. Joseph Blumenthal Ants and Evolution: The Non-Uniform Steiner Minimal Tree Problem – Chris Coulston, Matt Smith Katrina Werger GAMA: Genetic Algorithm Memory Assignment – Gary Grewal, Thomas C. Wilson Evolving Multiple Neural Networks – Sunghwan Sohn, Cihan Dagli

Sampling the Nature of a Population: Punctuated Anytime Learning for Co-Evolving a Team Training robots to push a box Separate populations for specialization Each gene represents a sub-cycle and a number of times to be repeated Punctuated Anytime Learning – allows learning system to be periodically updated throughout simulated evolution Evaluate individual performance regularly, with punctuated team evaluation Use a sample of high ranked individuals from population a to evaluate population b Results – Population of 64, sampling rate of 8 generations, new alphas selected every 100 generations Good level of success

Ants and Evolution: The Non-Uniform Steiner Minimal Tree Problem Given a set of points G, the Steiner Minimal Tree (SMT) seeks a minimal cost collection of edges spanning G, incorporating extra Steiner points not in G Hexagonally partitioned plane Steiner tree is MST on set of given and Steiner points GA generates Steiner points and uses a MST algorithm for evaluation Mutation adds and removes Steiner points, crossover by merging compatible full components Ant colonies work with preferred direction, scent trail, scent decay, forage and follow Ants find another city or fall off edge, teleporting back home to start again Steiner points added at scent crossing points Result: GA beats Ant Colony

GAMA: Genetic Algorithm Memory Assignment Multi-bank memory assignment for DSPs Operands fetched from same memory must be done sequentially, operands from separate memories in parallel Hard constraints and Soft Constraints Fitness = number of hard constraints satisfied + benefit of satisfied soft constraints Directed mutation applied to eliminate hard constraint violations Runs unsupervised – mutation and crossover rates determined and adjusted automatically Currently used in Cogen, a retargetable, optimizing compiler for DSPs

Evolving Multiple Neural Networks Neural networks have a complex structure that gives power to solve difficult problems, but has ambiguity of how to construct proper architecture GA used to locate optimal architecture GA selects feature subset and evolves topology Binary GA for layer connectivity Fitness function chosen to maximize correct ratio and minimize complexity Networks combined using average method – individual subnetworks make up population of GA Additional GA used to select population members to make up final network Results: experiments show an improvement in performance and topology vs. classical neural network