https://github.com/enricoschumann/neighbours
Neighbourhood Functions for Local-Search Algorithms
https://github.com/enricoschumann/neighbours
algorithms local-search machine-learning neighbourhood-functions neighbours simulated-annealing subset-selection threshold-accepting
Last synced: 2 months ago
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Neighbourhood Functions for Local-Search Algorithms
- Host: GitHub
- URL: https://github.com/enricoschumann/neighbours
- Owner: enricoschumann
- Created: 2018-12-17T14:44:21.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-09-22T05:23:54.000Z (over 1 year ago)
- Last Synced: 2024-01-28T09:42:53.538Z (about 1 year ago)
- Topics: algorithms, local-search, machine-learning, neighbourhood-functions, neighbours, simulated-annealing, subset-selection, threshold-accepting
- Language: R
- Size: 69.3 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog
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README
# neighbours
Neighbourhood functions are key components of
local-search algorithms such as Simulated Annealing or
Threshold Accepting. These functions take a solution
and return a slightly-modified copy of it, i.e. a
neighbour. The package provides a single function,
neighbourfun(), that produce a neighbourhood function,
based on various parameters such as minimum or maximum
values. Currently supported are numeric and logical
solutions. The algorithms were originally created for
portfolio-optimization models, but can also be used for
other models.
[ [More] ](http://enricoschumann.net/R/packages/neighbours/)
## Installing the package
The latest released version is available from
http://enricoschumann.net. In an R session, just type:
install.packages('neighbours,
repos = c('http://enricoschumann.net/R', getOption('repos')))