Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems

We develop and evaluate a cloud scale distributed covariance matrix adaptation based evolutionary strategy for problems with dimensions as high as 400. We adopt an island based distribution model and rely on a peer-to-peer communication protocol. We identify a variety of parameters in a distributed island model that could be randomized leading to a new dynamic migration protocol that can prove advantageous when computing on the cloud. Our approach enables efficient and high quality distributed sampling while mitigating the latencies and failure risks associated with running on a cloud. We evaluate performance on a real world problem from the domain of wind energy: wind farm turbine layout optimization.