2018/12/06 by F. Brglez, Brglez, Franc
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Matrix Theory and Algorithms #Neural Networks and Reservoir Computing #Quantum Computing Algorithms and Architecture
paper · pdf · doi:10.48550/arxiv.1812.03075
openalex publication_date 2018/12/06 · openalex created_date 2019/07/12 · openalex updated_date 2026/07/28
A rigorous empirical comparison of two stochastic solvers is important when one of the solvers is a prototype of a new algorithm such as multiwalk (MWA). When searching for global minima in ℝp, the key data structures of MWA include: p rulers with each ruler assigned m marks and a set of p neighborhood matrices of size up to m(m-2), where each entry represents absolute values of pairwise differences between m marks. Before taking the next step, a controller links the tableau of neighborhood matrices and computes new and improved positions for each of the m marks. The number of columns in each neighborhood matrix is denoted as the neighborhood radius rn ≤ m-2. Any variant of the DEA (differential evolution algorithm) has an effective population neighborhood of radius not larger than 1. Uncensored first-passage-time performance experiments that vary the neighborhood radius of a MW-solver can thus be readily compared to existing variants of DE-solvers. The paper considers seven test cases of increasing complexity and demonstrates, under uncensored first-passage-time performance experiments: (1) significant variability in convergence rate for seven DE-based solver configurations, and (2) consistent, monotonic, and significantly faster rate of convergence for the MW-solver prototype as we increase the neighborhood radius from 4 to its maximum value.