@inproceedings{e2a4f795115e497684ff3e9bf539e287,
title = "Online learning via congregational gradient descent",
abstract = "We propose and analyse a populational version of stepwise gradient descent suitable for a wide range of learning problems. The algorithm is motivated by genetic algorithms which update a population of solutions rather than just a single representative as is typical for gradient descent. This modification of traditional gradient descent (as used for example in the backpropagation algorithm) avoids getting trapped in local minima. We use an averaging analysis of the algorithm to relate its behaviour to an associated ordinary differential equation. We derive a result concerning how long one has to wait in order that with a given high probability, the algorithm is within a certain neighbourhood of the global minimum. We also analyze the effect of different population sizes. An example is presented which corroborates our theory very well.",
author = "Blackmore, \{Kim L.\} and Williamson, \{Robert C.\} and Mareels, \{Iven M.Y.\} and Sethares, \{William A.\}",
note = "Publisher Copyright: {\textcopyright} 1995 ACM.; 8th Annual Conference on Computational Learning Theory, COLT 1995 ; Conference date: 05-07-1995 Through 08-07-1995",
year = "1995",
month = jul,
day = "5",
doi = "10.1145/225298.225330",
language = "English",
series = "Proceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995",
publisher = "Association for Computing Machinery (ACM)",
pages = "265--272",
booktitle = "Proceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995",
address = "United States",
}