We show how and why using genetic operators that are applied with probabilities that depend on the fitness rank of a genotype or phenotype offers a robust alternative to the Sim...
Solving complex, real-world problems with genetic programming (GP) can require extensive computing resources. However, the highly parallel nature of GP facilitates using a large n...
—The non-data components of a visualization, such as axes and legends, can often be just as important as the data itself. They provide contextual information essential to interpr...
We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
Abstract. We provide a framework for the design and analysis of dynamic programming algorithms for surface-embedded graphs on n vertices and branchwidth at most k. Our technique ap...