Search Results for "parallel genetic algorithm"

The source code has been migrated and is now hosted on Github: https://github.com/jenetics/jenetics Jenetics is an advanced Genetic Algorithm, Evolutionary Algorithm and Genetic Programming library, respectively, written in modern day Java. It is designed with a clear separation of the several algorithm concepts, e. g. Gene, Chromosome, Genotype, Phenotype, Population and fitness Function. Jenetics allows you to minimize or maximize the given fitness function without tweaking it. ...

Even in the simplified two dimensional HP-model (hydrophob/polar) the prediction of proteinfolding is NP complete. We implement a brute force algorithm with serial and parallel execution to solve short inputs of HP sequences (0-1 bitstrings). Selbst im vereinfachten zweidimensionalen HP-Modell (hydrophob/polar) ist die Proteinfaltung bereits NP-vollständig. Hier implementieren wir einen brute-force Algorithmus zur Lösung kurzer Eingabesequenzen (0-1-Bitstrings) für die Proteinfaltung. ...

GeneticAlgorithms is a simple and lightweight framework to implement an optimization heuristic following the Genetic Algorithms model. A genetic algorithm mimics the natural processes of evolution, selection and "survival of the fittest". This framework is intuitive and good integrated with Java 1.5 SDK and later.

The first parallel Smith-Waterman algorithm exploiting Intel Xeon Phi clusters to accelerate the alignment of long DNA sequences. This algorithm is written in C++ (with a set of SIMD intrinsic extensions), OpenMP and MPI. The performance evaluation revealed that our algorithm achieves very stable performance, and yields a performance of up to 30.1 GCUPS on a single Xeon Phi and up to 111.4 GCUPS on four Xeon Phis sharing a host.

The first parallel algorithm to accelerate the Smith-Waterman protein database search on shared-host multiple Intel Xeon Phi coprocessors. By searching against the UniProtKB/TrEMBL database, SWAPHI achieves a performance of up to 58.8 billion cell updates per second (GCUPS) on a single Xeon Phi and up to 228.4 GCUPS on four Xeon Phis.

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.

DecGPU (Distributed Error correction on GPUs) is a parallel and distributed error correction algorithm for large-scale short read assembly. It is implemented using CUDA C++ and MPI, running on a GPU cluster.

This library is a lightweight implementation of genetic algorithm, contains the most popular types of chromosomes and the basic algorithms for selection, elitism, crossing and mutation.

Java API for implementing any kind of Genetic Algorithm and Genetic Programming applications quickly and easily. Contains a wide range of ready-to-use GA and GP algorithms and operators to be plugged-in or extended. Includes Tutorials and Examples.

DrPangloss is a python implementation of a three operator genetic algorithm, complete with a java swing GUI for running the GA and visualising performance, generation by generation

Molevolve is a Java library for running a Genetic Algorithm to model the 3-dimensional structures of peptide chains from amino-acid sequences. Client code can specify its own peptide chain model, fitness functions and GA operations. Requires JDK 1.5.

IslandEv distributes a Genetic Algorithm (like <a href="/projects/jaga">JaGa</a>) across a network (see <a href="/projects/distrit">DistrIT</a>) using an island based coevolutionary model in which neighbouring islands swap migrating individuals every

Galileo is a library for developing custom distributed genetic algorithms developed in Python. It provides a robust set of objects that can be used directly or as the basis of derived objects. Its modularity makes it easy to extend the functionality. The

MAGMA: Multiobjective Analyzer for Genetic Marker Acquisition A genetic algorithm for generating SNP tiling paths from a large SNP database based on the competing objectives of cost (number of SNPs) and coverage (haplotype blocks): Hubley R., Zitzler

The SBMLevolver is an evolutionary algorithm package that creates SBML models with user-specified properties and behaviour from a given set of building blocks. Applications lie in network reconstruction and synthetic biology.