Stochastic Diffusion Search
Project description
A library which implements the main variants of Stochastic Diffusion Search (SDS), and provides a convenient front end.
Stochastic Diffusion Search (SDS) is a generic population-based search method. SDS agents perform cheap, partial evaluations of a hypothesis (a candidate solution to the search problem). Hypotheses with the potential to be strong solutions are then diffused through the swarm through direct one-to-one communication. As a result of the diffusion mechanism, high-quality solutions can be identified from clusters of agents with the same hypothesis.
This is a library used during the writing of my PhD thesis, full documentation and code are both published online.
SDS has a Scholarpedia page: http://www.scholarpedia.org/article/Stochastic_diffusion_search
A list of papers written on SDS can be found in the Stochastic Diffusion Search paper repository, maintained by the author of this module: http://aomartin.ddns.net/sds-repository/publications.html
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file sds-2.0.1.tar.gz
.
File metadata
- Download URL: sds-2.0.1.tar.gz
- Upload date:
- Size: 193.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: Python-urllib/3.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 606f9b97201ba06f3c5e63701b34225e540e40ce3ab3857906e3a2985ea68ae1 |
|
MD5 | 3d86cae34f3895a61d67f5b0d861a9f1 |
|
BLAKE2b-256 | 047705a0b055ab9a85fc293d5db510dbca3c1ff080fb8c7948100242a3d5ab76 |