Search Results for "parallel genetic algorithm" - Page 2

The program puts beats through evolution - survival, mutations, reproduction. The purpose, in the words of Africa Bambaataa - Looking for the perfect beat. Using a genetic­ algorithmic framework, the user's taste defines a Beat fitness function - she decides which beats survive and breed, and which join the choir invisible. In addition, the user can easily define an automatic fitness function, put the program on 'auto­pilot' mode, and let it speed up evolution. Different types of...

MapPSO is a tool for Ontology Alignment, which uses Discrete Particle Swarm Optimisation. A particle swarm is used to search for the optimal alignment. The algorithm is massively parallel and adapts naturally on parallel architectures.

...It's all very randomly generated, and there is no way for any user input. I'll consider putting some in later. I had been wanting to make the Game of Life for some time as well as make some kind of genetic algorithm based code. So, here is what I came up with. While this may just seem like simplify a graphical display of what boredom looks like... well, it really doesn't go much past that point. If you Don't know what Conway's game of life is: It's the Black (Or white, I may have changed them) cells that follow a simple set of instructions based on the state of its adjacent cells. ...

This is a toolbox to run a GA on any problem you want to model. You can use one of the sample problems as reference to model your own problem with a few simple functions. You can collaborate by defining new example problems or new functions for GA, such as scaling, selection or adaptation methods. In that case, you should then include your credits in the file, upload it to matlab central and contact the author. Suggestions are also welcome but naturally I won't be able to attend...

Darwin 2: Java Framework for Evolutionary Computation (genetic algorithm, GA). A true framework with out-of-the-box functionality and extensibility of all classes. Interface-based pattern with dependency-injection to configure components.

See the new project page: https://nojhan.github.io/paradiseo/ (Archived project page: http://eodev.sourceforge.net/)

This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.

CRFSharp(aka CRF#) is a .NET(C#) implementation of Conditional Random Fields, an machine learning algorithm for learning from labeled sequences of examples. It is widely used in Natural Language Process (NLP) tasks, for example: word breaker, postagging, named entity recognized, query chunking and so on. CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. ...

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems. To address this problem, we present an efficient genetic algorithm by the combination with clustering methods. The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and.

GeneThello (read jə-ˈne-ˈthe-lō), is an acronym for genetic othello, an othello (reversi) playing program which based on Genetic Algorithm (GA). In principle GeneThello consist of an othello program and a genetic algorithm system.

Project aim to provide simple easy APIs for Java developers to use interactive abilities in their Java Applications like speech recognition, handwriting recognition, use of web cam , sound record/play, decision trees , text to speech and many others.

Developed as a final year project at Islamic University of Technology (IUT) during 2010 - 2011 academic year. This code is a simple implementation of real coded genetic algorithm for numerical optimization. Code is written in C/C++.

BIL++ is a set of standalone C++ packages for data processing in Bioinformatics (Graph mining, Bayesian networks, Genetic algorithm, Discretization, Gene expression data analysis, Hypothesis testing).

A genetic algorithm in Python for evolving programs that write a given string to an allocated dataspace, using a made-up machine language with only 7 instructions and flow reversal.

This project is a complete cross-platform (Windows, Linux) framework for Evolutionary Computation in pure python. See the project site at http://pyevolve.sourceforge.net or the blog at http://pyevolve.sourceforge.net/wordpress

This project intends to create a bacteria simulator framework, with some realistic bacteria control methods based on chemical signaling, simple sensors, motors and neural networks. The bacteria will evolve in a genetic algorithm environment.

X-GAT (XML-based Genetic Algorithm Toolkit) is a Java framework to optimize problems with Genetic Algorithms (GAs). Differently from other frameworks, X-GAT contains ready-to-use GAs implementations and new features can be easily added.

Mainly include the codes of genetic algorithm, interative genetic algorithm, that are written in Java Applcations also included such as function optimization, simple fashion design optimization, face optimization and so on

Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.

Parallel Reinforcement Evolutionary Artificial Neural Networks (PREANN) is a framework of flexible multi-layer ANN's with reinforcement learning based on genetic algorithms and a parallel implementation (using XMM registers and NVIDIA's CUDA).

Simple Genetic Algorithm API used in an university work...

Game Toolbox is a C# assembly designed to facilitate the creation of games and game prototypes. It contains no graphics code, does not depend on XNA, and is Mono-compatible. It provides implementations of A*, GOAP, a genetic algorithm, and more.

Travelling Salesman Problem using Genetic Algorithm.

This easy to run and change program for developers, it contains several evolutionary based strategies allowing you try among some function samples

POGA - Parameter's Optimization by Genetic Algorithm - Developed by Leonardo Santos (LAC-INPE :: santoslbl@gmail.com) and José Miranda (IF-UFBA :: vivas@ufba.br). 114 Downloads of the first version. Second version avaliable.