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Primarily applied in experimental psychology, where it is used to create experiment schedules. It even can be applied to other planning-activities that have to met boundary constraints. Rando uses a genetic-algorithm to construct a fulfilled schedule.
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 geneticalgorithm and Markov simulations are currently implemented.
Searches for adecuate design for feedforward backpropagation neural network, employing geneticalgorithm as refining engine. The result topolgy may not be orthodox.
The Distributed Genetic Programming Framework is a scalable Java genetic programming environment. It comes with an optional specialization for evolving assembler-syntax algorithms. The evolution can be performed in parallel in any computer network.
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PyLife is an implementation of the game of life algorithm featuring parallel programming. It uses MPI and python to achieve a consistent software architecture and reliably performance.
A flexible programming library for evolutionary computation. Steady-state, generational and island model genetic algorithms are supported, using Darwinian, Lamarckian or Baldwinian evolution. Includes support for multiprocessor and distributed systems.
DrPangloss is a python implementation of a three operator geneticalgorithm, complete with a java swing GUI for running the GA and visualising performance, generation by generation
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NullAllEst is the implementation of a maximum likelihood algorithm to estimate the frequency of a null allele in microsatellite genetic data. A Markov Chain Monte Carlo simulation is used to solve the likelihood function.
Molevolve is a Java library for running a GeneticAlgorithm 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 GeneticAlgorithm (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 geneticalgorithm 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
PPAT, or Parallel Path following Algorithm using Triangles, is a reliable parallel tool to trace the level curves of any continuous, not necessarily smooth, function f(z): C → R.
Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (geneticalgorithm) wrappers for feature selection. WEKA 3 interfaces are in development.
A GeneticAlgorithm Training System in Python.
Gatspy provides the framework, the user provides the error
(fitness) function. Gatspy will evolve a solution that
attempts to minimize the error.
aVolve is an evolutionary/geneticalgorithm designed to evolve single-cell organisms in a micro ecosystem. It currently uses the JGAP Geneticalgorithm, but does include a primitive geneticalgorithm written in Python.
GAZES - GeneticAlgorithm Zombie Eradication Simulation Grid-based simulator for zombie scenario. The units will use geneticalgorithm to determine their actions on a cycle by cycle basis. This is a school project, so is likely on hiatus for summer.
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.
A genetic algoritm based system for generating profiled endwalls.
This code is based on the Durham Endwall Design System used to produce endwall designs for low speed linear cascades. It consists of a number of Octave scripts which run mesh generators and flow solvers and uses a geneticalgorithm to find the optimal solution.