The SA (Simulated Annealing Layer Package) PackageThis package includes the Simulated Annealing Engines and all the interfaces and base classes needed for an Simulated Annealing search.
This package includes the Simulated Annealing Engines and all the interfaces and base classes needed for a
Simulated Annealing search.
The Simulated Annealing (
SAEngine) will perform all the work. There are different
for local or peer-to-peer-based evolution, for example.
This package provides the basic ability of searching solutions using the Simulated Annealing algorithm for any type of problems. Multi-objective Simulated Annealing algorithms are be implemented using different distribution schemes in this package which bases on the versatile search api provided by the Search-API-package.
The idea behind searching problem solutions using Simulated Annealing is the following: You have an idea on some qualities a thing should have, but you donít know what this thing is or how it should be created. The Simulated Annealing approach might answer this question. At first, a random thing is created. We modify it slightly and do this again and again, until we have found the ideal thing or we are close enough to what we want. Simulated Annealing modifies a thing, and if the new thing better than the old, it is kept. Otherwise, we keep the old thing with a given probability. For more information, visit the Genetic Programming Group at http://groups.google.de/group/geneticprogramming/.
Features of this package:
We support four different types of distribution of computational load for our Simulated Annealing:
Search algorithms are executed by
SearchEngines. The names of the
search engines are formed like this: The distribution shortcut + the algorithm
This Open-Source research project is licensed under LGPL, a license even more liberate than the GPL.
One feature, especially important for developers, is the full documentation of the source code provided. Each method, class, package is fully documented down to privated declares. No parameter is undocumented. We are also happy about hints or request for more documentation, because we want that anyone can use our code and hopefully understand it. This project has also educational character.
You can find more information on the project's home page or on the project's page on SourceForge. The source code and examples can be downloaded at the downloads page. Useful information and news will frequently be published in the Genetic Programming Group.