Search Results for "parallel genetic algorithm" - Page 5
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Showing 319 open source projects for "parallel genetic algorithm"
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Grammatical Optimization
A java based framework for grammatical evolution.
This project is a result for my curiosity for how grammatical evolution (GE) works. Eventually, I made this wrapper for GE that should work with any numerical optimization algorithm. So the idea behind GE is that it takes production rules for computer programs using a context free grammar in Backus Naur form. The production rules can be used to evolve computer programs by running an algorithm such as a genetic algorithm or a particle swarm optimizer in the background. -
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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.
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FamSeq
Variant calling on the basis of pedigree information
...FamSeq accommodates de novo mutations and can perform variant calling at chromosome X. To accommodate variations in data complexity, FamSeq consists of three distinct 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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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 free form deformation model. ... -
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SimpleFB_GA
a simple genetic algorithm written in FreeBasic
a FreeBasic-written Genetic Algorithm code is here deployed -
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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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HfT GA RCPSP
Genetic Algorithm for the RCPSP
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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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niGA
Heterogenous Multiprocessor Scheduling Using Genetic Algorithms
Implementation of task scheduling using Genetics algorithm for heterogeneous parallel programming -
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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, in Department of Computer Science at Federal University of Lavras. ... -
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VariantMaster
Extract causative variants for monogenic and sporadic genetic 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) data to robustly estimate the conditional probability of segregation in a family, as well as the probability of it being de novo or somatic. In familial cases, various modes of inheritance are considered: X-linked, autosomal dominant, and recessive (homozygosity or compound heterozygosity). ... -
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Bin Packing with Genectic Algorithm
Bin Packing problem solved using Genectic Algorithm
This project contains a solution for a Bin Packing problem solved using Genectic Algorithms. The code in the project was created as a solution for a problem in a combinatorial optimization class at the Univeridade Federal do Rio Grande do Sul (UFRGS - Brasil) in 2007. -
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OBSOLETE s-ga
genetic algorithm
Obsolete. Moved to the new Project 'sgena' -
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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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Evochumps
Evolving recursive artificial neural networks in a simulation.
...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 particular RNN in real time. -
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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. -
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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). ...
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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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PPSeq: Parallel NGS Analysis
Parallel Processing for Next-Generation Sequencing (NGS) Analysis
High-throughput next generation sequencing (NGS) technology has quickly emerged as a powerful tool in many aspects of biomedical research. However, along with its rapid development, the data magnitude and analysis complexity for NGS far exceed the capacity and capability of traditional small-scale computing facilities, such as multithreading algorithms on standalone workstations. To address this issue, here we present a solution using the ever-increasing supply of processing power by massive... -
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ABM-Calibration-SensitivityAnalysis
Codes and Data for Calibration and Sensitivity Analysis of ABM
...<http://jasss.soc.surrey.ac.uk/xx/x/x.html> Methods/Techniques used are: a. Parameter 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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Model Based Test Generator
Model Based Test Generator from UML
The purpose of this project is creating test cases from uml statemachine and Class diagram with OCL constraint. We use forward search strategy to creating abstract test cases and using genetic algorithm for creating test data. -
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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 OpenCL SDK. ... -
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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
...We use CUDA , a general purpose parallel programming platform that allows the usage of NVIDIA GPUs to solve complex problems in an efficient way. -
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Tetris AI
A try to demonstrate an AI module based on doubled genetic algorithm.
