Abstract: In its traditional form, the cyclic genetic algorithm (CGA) was found to be a successful method for evolving single loop control programs for legged robots. Its major limitation was the ...

Abstract: A new scheduling algorithm for dataflow graphs with nested conditional branches is presented. The algorithm employs a bottom-up approach to transform a dataflow graph with conditional ...

The k nearest neighbor (kNN) approach is a simple and effective nonparametric algorithm for classification. One of the drawbacks of kNN is that the method can only give coarse estimates of class ...

Here is a simple example of using GAS with an oracle d-separation tester to learn the essential graph from a randomly generated directed acyclic graph. The test suite validates the algorithm's ...

As a supervised machine learning algorithm, conditional random fields are mainly used for fault classification, which cannot detect new unknown faults. In addition, faulty variable location based on ...

This R package implements a conditional maximization algorithm with the graphical horseshoe prior for network, covariance matrix, and precision matrix (inverse of the covariance) estimation. The ...

ABSTRACT: We present a new derivative-free optimization algorithm based on the sparse grid numerical integration. The algorithm applies to a smooth nonlinear objective function where calculating its ...