Search Results for "genetic algorithm path"

...By default, the class "GeneticCode" generates alternative genetic codes, with the reqirement of block structure, and, optionally, with the assumption of stereochemical or biosynthetic models (to impose the assumption of the adaptive model, simply filter the codes using the error_cost() function). See Appendix in Makukov & shCherbak (2017) for the description of the algorithm. The code requires Qt 5.

Genetic Architecture Model Emulator for Testing and Evaluating Software (GAMETES) is an algorithm for the generation of complex single nucleotide polymorphism (SNP) models for simulated association studies. GAMETES is designed to generate epistatic models which we refer to as pure and strict, that constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model.

...The model used in the application is based on two significant papers: 1.1997 Pablo Umana, James E.Bailey A Mathematical Model of N-linked Glycoform Biosynthesis. 2.2005 Frederick J.Krambeck, Michael J.Betenbaugh A Mathematical Model of N-linked Glycosylation. The application allow to simulate the network in dynamic form with various ODE solvers(Euler, Runge-Kutta etc). Genetic Algorithm is used to adjust the kinetic parameters of Michaelis-Menten equations in glycosylation reaction network. A dynamic plot is supplied to show the value of mole amout and mole fraction of various glycoform. Thanks to Alexey Balakin and Alessandro Presta. Alexey Balakin supply mathgl(https://sourceforge.net/projects/mathgl) that make the implementation of dynamic plot in this application easily. ...

a FreeBasic-written Genetic Algorithm code is here deployed

As of 2012-12-18, this project may now be found at http://http://code.google.com/p/paba JECO is a Evolutionary Computation Library deveoloped in Java. It includes a variety of evolutionary optimization techniques such as genetic algorithm, genetic programming, evolutionary mapping methods, particle swarm optimization, ant colonies, etc.

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

PGAF provides a framework tuned, user-specific genetic algorithms by handling I/O, UI, and parallelism. It is designed for optimizing functions that take a "very long time" to evaluate.

A 2D rigid physics simulator and set of genetic algorithm tools. Evolve cute little walking creatures.

The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.

A framework for utilising the Genetic Algorithm in the domain of Game Theory

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.

aVolve is an evolutionary/genetic algorithm designed to evolve single-cell organisms in a micro ecosystem. It currently uses the JGAP Genetic algorithm, but does include a primitive genetic algorithm written in Python.

GAZES - Genetic Algorithm Zombie Eradication Simulation Grid-based simulator for zombie scenario. The units will use genetic algorithm to determine their actions on a cycle by cycle basis. This is a school project, so is likely on hiatus for summer.

...Visitors move, go online and offline, and build a dynamic network. One can say a complex adaptive network emerges, with all its problems. We tackle these problems by introducing a bio-inspired algorithm, which lets the content place itself where it is needed. It is robust, scalable and adaptive. The transport path of content is exploited to replicate content and limited storage space is handled by clean-up mechanisms. We implemented a simulator to show the algorithm's performance and to test its behavior under real circumstances. ...