Online learning via congregational gradient descent
type: Item
instance of: scholarly article
title: Online learning via congregational gradient descent
zbMATH Open document ID: 0891.93091
DOI: 10.1007/BF01211551
author: Kim L. Blackmore
author: Robert C. Williamson
author: William A. Sethares
author: Iven Mareels
published in: MCSS. Mathematics of Control, Signals, and Systems
publication date: 1998-02-11T00:00:00Z
review text: The paper studies a version of the stepwise gradient descent algorithm consisting in the update of a population of solutions. The algorithm allows the determination of the global minimum of the expected cost function and is suitable for a broad class of learning and optimization problems. An averaging analysis of the algorithm allows to study its behaviour in terms of an associated ordinary differential equation. The speed of convergence and the effect of different population sizes are also studied. Simulation examples are finally provided.
Mathematics Subject Classification ID: 93E35
Mathematics Subject Classification ID: 93E99
zbMATH DE Number: 1109544
zbMATH Keywords: genetic algorithms
zbMATH Keywords: online learning
zbMATH Keywords: gradient descent
zbMATH Keywords: optimization
zbMATH Keywords: averaging analysis
reviewed by: Giovanni Di Masi
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1109544