Open Source Algorithms - Page 25

This is an attempt to use Genetic Programming for writing a go player in a pseudo-ASM. It is entirely written in Common Lisp.

GP@home is a new group/project dedicated to fully utilizing the combined concepts of genetic programming and distributed computing to improve the quality of life for all human beings. Nothing is impossible working together.

Genetic Programming Classifier is a distributed evolutionary data classification program. It uses the ensemble method implemented under a parallel co-evolutionary Genetic Programming technique.

Genetic Programming (tree structure) predictor within Weka data mining software for both continuous and classification problems.

Genetic Programming in OpenCL is a parallel implementation of genetic programming targeted at heterogeneous devices, such as CPU and GPU. It is written in OpenCL, an open standard for portable parallel programming across many computing platforms.

The SP is a tool is a tool to research for automatic synthesis of protocols using genetic programming. This tool will allow the construction of heuristics to apply techniques simulation and validation of models.

This project aims at providing a set of tools for solving the class of monodimensional packing problems (such as cutting stock, bin packing and knapsack problem) mainly using genetic algoritms.

Geneur is an Open Source scheduler for GRID. It is based on variation of genetic algorithms. Geneur uses backfill scheduling algorithm to create first genetic population.

Implementation of an Interval Iterative Closest Point that uses intervals to found the global optimal solution. ICP can be used in vision technologies and photogrametry as pattern mathcing and as geometrical model finder.

Cell Broadband Engine Processor optimized libraries and software from Georgia Tech, for a number of libraries for FFT, MPEG, compression, and encryption. This project also includes patches to accelerate R on the Cell Broadband Engine processor.

A C++ RAII ( Resource Allocation Is Initialization ) implementation of an automatic pointer/reference. Pointable/Referenced objects are handled in a manner similar to Python. Strives to have the simplest syntax possible.

in-place sorting algorithm with O(n*log(k)) time komplexity. The data have to be the structure: struct { unsigned (char/short/int/long); [...] }

This project aims to test Distributed Artifitial Intelligence in a urban traffic light control. GlobalSyncLocal is used to interact with the SUMO simulator through the TraSMAPI.

The game of Cows-n-Bulls is back ! Gnu-Moo aims to provide a command line as well as GTK+ front-end for the user to play the cows-n-bulls game. Multi-game sessions and relevant session stats are provided, which can be useful to test heuristic algorithms.

GoDS, which means "Go Data Structures", is an implementation of various data structures and algorithms in Go. It provides a wide variety of containers (Sets, Lists, Stacks, Maps, Trees), sets (HashSet, TreeSet, LinkedHashSet), lists (ArrayList, SinglyLinkedList, DoublyLinkedList), stacks (LinkedListStack, ArrayStack), maps (HashMap, TreeMap, HashBidiMap, TreeBidiMap, LinkedHashMap), trees (RedBlackTree, AVLTree, BTree, BinaryHeap), comparators, and iterators. Containers are either ordered or unordered. All ordered containers provide stateful iterators and some of them allow enumerable functions. A list is a data structure that stores values and may have repeated values. A list backed by a dynamic array that grows and shrinks implicitly. A list where each element points to the next element in the list. A set is a data structure that can store elements and has no repeated values. A Map is a data structure that maps keys to values.

Provide a .NET object model to support computation of minimum, maximum, average of primitive types exported through properties by any object.

GraphScript project is a network theory simulator based purely on Java and Java scripting engines. It is intended to allow fast development, testing and visualization of network theory (graph) algorithms.

This project aims to provide developers a convenient library about graph theory algorithm and some basic data structures. It will be developed in Java via Eclipse IDE, supporting both win32 and linux systems, and published as jar files.

A Web based IDE that supports nodes and edges (graphs) with attributes. An interpretor executes your graphs. Views of graphs allow developers to see references to variables, easily see the path of execution and rearrange the structure of data and program

A Java library and set of tools to quickly and easily manipulate graphs (set of nodes and edges) and dynamic graphs. The principal trait of this library is its ability to manage (create, edit, layout, display) continuously evolving graphs. The development of this project now takes place on Github. Visit the official Graphstream website at http://graphstream-project.org

Graphal is an interpreter of a programming language that is mainly oriented to graph algorithms. There is a command line interpreter and a graphical integrated development environment. The IDE contains text editor for programmers, compilation and script output, advanced debugger and visualization window. The progress of the interpreted and debugged graph algorithm can be displayed in 3D scene.

This project consists of various implementations of the Graphplan algorithm. At the moment we have two implementations of Graphplan: * Emplan - A C++ implementation for Linux, Windows and MacOS X * JavaGP - A Java implementation

Graphserver provides fast shortest-path computations on very large graphs, for use as a sub-component of a trip-planning service.

GuidLib is a library with functions for GUIDs (also known as UUIDs, as defined by RFC 4122).

Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. These environments have a shared interface, allowing you to write general algorithms.