Search Results for "parallel genetic algorithm" - Page 4
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Showing 237 open source projects for "parallel genetic algorithm"
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Parallel Neural Networks
Neural networks in CUDA & OpenCL with back propagation algorithm
This project is my engineering diploma. It's aim is to compare the efficiency of both technologies and to check where which hacks works better. What is more one of my tasks is to compare different ways of decomposing computations in parallel. -
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Ezys
Ezys 3D medical image registration program
Ezys is a non-linear 3D medical image registration program. Ezys fully exploits the parallel computing power of inexpensive commercial graphics processing units (GPU), resulting in a very fast and accurate program capable of running on desktop PCs and even some laptops. On these systems, non-linear image registrations take less than a minute to complete. Ezys implements a diffeomorphic inverse consistent image registration algorithm with a demons-style regularization based on a non-parametric... -
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FamSeq
Variant calling on the basis of pedigree information
... implementations of the Mendelian genetic model: the Bayesian network algorithm, Elston-Stewart algorithm and Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA® graphics processing unit. -
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NEAT Visualizer SFML
A NEAT Implementation and Visualization System
Evolves neural networks using the Neuro-Evolution of Augmenting Topologies (NEAT) technique. A separate visualization system uses another genetic algorithm to evolve images of the otherwise dimensionless networks so their structure can be observed. -
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Distributed Multithread Apriori (DMTA)
A parallel implementation using MPI and OpenMP to Apriori algorithm
DMTA (Distributed Multithreaded Apriori) is a parallel implementation of Apriori algorithm, which exploits the parallelism at the level of threads and processes, seeking to perform load balancing among the cores. Was implemented in C++ language, using the parallelization libraries OpenMP and MPI. The algorithm was generated as a result of a project developed by André Camilo Bolina, under the guidance of teachers Marluce Rodrigues Pereira, Ahmed Ali Abdalla Esmin and Denilson Alves Pereira... -
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VariantMaster
Extract causative variants for monogenic and sporadic genetic diseases
There is a fast growing interest in clinical genetics to the utilization of High Throughput Sequencing data for accurate diagnosis of monogenic diseases. To improve the identification of the variants from HTS, we developed VariantMaster, an original program that accurately and efficiently extracts causative variants in familial and sporadic genetic diseases. The algorithm takes into account predicted variants (SNPs and indels) in affected individuals or tumor samples and utilizes the row (BAM... -
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niGA
Heterogenous Multiprocessor Scheduling Using Genetic Algorithms
Implementation of task scheduling using Genetics algorithm for heterogeneous parallel programming -
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PPSeq: Parallel NGS Analysis
Parallel Processing for Next-Generation Sequencing (NGS) Analysis
... parallel processing (MPP) systems. -
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ALCHEMY is a genotype calling algorithm for Affymetrix and Illumina products which is not based on clustering methods. Features include explicit handling of reduced heterozygosity due to inbreeding and accurate results with small sample sizes
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ABM-Calibration-SensitivityAnalysis
Codes and Data for Calibration and Sensitivity Analysis of ABM
... fitting: 1. Full Factorial Design 2. Simple Random Sampling 3. Latin Hypercube Sampling 4. Quasi-Newton Method 5. Simulated Annealing 6. Genetic Algorithm 7. Approximate Bayesian Computation b. Sensitivity Analysis: 1. Local SA 2. Morris Screening 3. DoE 4. Partial (Rank) Correlation Coefficient 5. Standardised (Rank) Regression Coefficient 6. Sobol' 7. eFAST 8. FANOVA Decomposition Have also a look on our other projects: http://www.uni-goettingen.de/de/315075.html -
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Evochumps
Evolving recursive artificial neural networks in a simulation.
Evochups allows you to create a simple simulation environment where individuals called "chumps" display behavior according to their simulated brains. Each Chump has a recursive neural network which takes input from thier environment and controls the chump's behavior. It has a built-in evolutionary algorith to let the brains evolve conditoned to selective pressure. The program's interface allows you to manipulate all kinds of parameters of both the simulation, the genetic algorithm, and each... -
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The sequence alignment task in MAGI (magi.ucsd.edu) is based on the miRanda algorithm, but we redesign the miRanda algorithm on GPU by taking its advantages of massively parallel computing and extra high memory bandwidth using using NVIDIA’s Compute Unified Device Architecture (CUDA). The CUDA-miRanda implementation is a fast microRNA target identification algorithm that aligns short nucleotide sequences (i.e., < 32 nucleotides) against longer reference sequences (e.g., 20k nucleotides). It has...
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PyGAO
Genetic Algorithm Optimization for Python
A simple interface for performing genetic algorithm optimization for numerical problems. I am starting with a stripped-down version, where a solution can be described using a single vector of float numbers. Eventually, I will expand to more generic data structures and add multiple-species search options. For the time being, I have no plans of developing a GUI. For now, this is strictly a computational module. In addition to the standard Python libraries, PyGAO uses numpy. -
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GENIE (GEne-geNe IntEraction)
GPU based Parallel Gene-Gene Interaction Analysis
Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic... -
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gpgmpi
An GPGMP in C++/OpenCL with improved time step algorithm
The need for speed is always a challenge for large-scale stochastic simulation. This software is based on a parallel stochastic simulation algorithm already implemented on Graphics Processing Unit (GPU) for inhomogeneous reaction-drift-diffusion systems. We suggest an improved choice of time step which turns out to be almost 3 or even more times fast with nearly identical accuracy. The software is now completely implemented in C++ and OpenCL which only depends on boost library and GPU vendor’s... -
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NNBrain
A free, open source collection of neural network based AI agents.
NNBrain is a free and open source collection of artificial intelligence agents. These agents have applications in video games, research, business, and more. The included agents function in both discrete and continuous action and state spaces. -
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grn_inference_multigpu
This project is a exhaustive search algoritm for multiple GPUs
Gene regulatory networks (GRN) inference is an important bioinformatics problem in which the gene interactions need to be deduced from gene expression data, such as microarray data. <br> Feature selection methods can be applied to this problem. A feature selection technique is composed by two parts: a search algorithm and a criterion function. Among the search algorithms already proposed, there is the exhaustive search where the best feature subset is returned, although its computational... -
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Parallel DPCM
C# Parallel implementation of DPCM alogrithm
An implementation of the DPCM algorithm for compressing and decompressing an avi video file. It shows the compression size, as it is saved in a txt file near the avi original file. The decompression results in another avi file, saved near the original file. If the width and height of the frames aren't a multiple of 16, they are completed with black pixels. The pixel macroblocks of 16x16 are generated on separate threads for each frame and they are replaced on frames at decompression... -
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The parallelized FDTD Schrodinger Solver implements a parallel algorithm for solving the time-independent 3d Schrodinger equation using the finite difference time domain (FDTD) method. See the Hosted Apps > MediaWiki menu item for more information.
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Molecular Measurer
Parallel, Stochastic Measurement of Molecular Surface Area
A program to measure molecular surface area. The algorithm is GPU-accelerated using CUDA and is described in the research paper linked from the project home page. The input to the program is xyzr files, which can be generated from pdb files. -
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OpenAIL
Open Artificial Intelligence Library
[Open Artificial Intelligence Library]. This library main goal is to provide a tool box to those who want to use algorithm such as neural network or genetics Algorithm and all algorithms that are commonly within the Artificial Intelligence field. -
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AEvolution
Artificial life game
Artificial life game using a genetic algorithm in a simple 2D world. -
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Reactor Breeder
A Genetic Algorithm for Reactors in StarMade
This software uses a genetic algorithm to "evolve" reactor designs for Schema's space-simulation game, Star-Made (http://star-made.org/). One of the more unique aspects of the game is that ship power management is not as simple as filling a cube with power generation blocks. This mechanism leads to difficulties in getting maximal power generation out of such reactors. This program rectifies this issue by using a self-organizing variant of brute force search. Much of the search space... -
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Genetic Polynomials
genetic algorithm to approximate a set of points with polynomial
genetic algorithm to approximate a set of points with polynomial -
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Open Genetic Algorithm Toolbox
This is a MATLAB toolbox to run a GA on any problem you want to model.
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...
