Genetic Engineering

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

GeneThello (read jə-ˈne-ˈthe-lō), is an acronym for genetic othello, an othello (reversi) playing program which based on Genetic Algorithm (GA). In principle GeneThello consist of an othello program and a genetic algorithm system.

This is a cross-platform framework for using Genetic Algorithms for solutions. Written in Java and uses convinient plug-in features for every phase in the genetic development, while maintaining an easy-to-use API for easy integration into applications.


Platforms: Windows, Mac, Linux

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.


Platforms: Windows, Mac, Linux

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.


Platforms: Windows, Mac, Linux

An object oriented library of an Genetic Algorithm, implemented in Java. Clear separation of the several concepts of the algorithm, e.g. Gene, Chromosome, Genotype, Phenotype, Population and Fitness Function. The fitness calculation is parallelized.


Platforms: Windows, Mac, Linux

jrgp is a strong-typed Genetic Programming system, which features a graphical interface (gool) to setup and run GP-problems and a tool (fs-d) that greatly simplifies the definition of a GP-problem.


Platforms: Windows, Mac, Linux

An optimized java library for genetic algorithms. The library is designed to be fast and memory light, but still very easy to use.


Platforms: Windows, Mac, Linux

The Python Genetic Programming Project implements a Genetic Programming System a la J Koza in Python.


Platforms: Windows, Mac, Linux

This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.


Platforms: Windows, Mac, Linux

Round Trip Engineering Objects.Reverse object oriented source code (Java, C#, PHP5) into XOL (XML Object Language) DOM repository.Validate syntax using XML Schema. XSLT for code refactoring & code generation. IDE Editors available (Eclipse plugin)


Platforms: Windows, Mac, Linux

Springbots is a python application which takes a set of 2d physical structures built with nodes and movable springs and evolve them for specific tasks like walking, swimming and jumping using genetic algorithm.


Platforms: Windows, Mac, Linux

Trilobots is an arcade-style shooter that evolves the abilities and programming of the enemies using a genetic algorithm. The end goal is to make the evolution transparent, allowing users to learn how genetic algorithms work while having fun.


Platforms: Windows, Mac, Linux

VKAS uses genetic programming methods to find out the relationship among a set of independent and dependent variable. It is a problem solver software which uses evolutionary computing methods.


Platforms: Windows, Mac, Linux

M-files accompanying the " Genetic Algorithms & New Optimization Methods in MATLAB " webinar.These files provide what you need to run the two demos: Optimization of non-smooth objective function, and Optimization of a random stochastic objective function.To run the first demo run the file...


Platforms: Matlab

This zip file contains the Presentation (PDF) and M-files that were demonstrated in the MathWorks Webinar: Using Genetic Algorithms in Financial Applications delivered on Dec 11 2007.The purpose of the webinar was to highlight how Genetic Algorithms may be used to supplement portfolio...


Platforms: Matlab

Introduction to Stochastic Search and Optimization is an overview of the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The book may serve as either a reference book for researchers and...


Platforms: Matlab

This code proposes genetic algorithm (GA) to optimize the point-to-point trajectory planning for a 3-link (redundant) robotarm. The objective function for the proposed GA is to minimizing traveling time and space, while not exceeding a maximumpre-defined torque, without collision with any...


Platforms: Matlab

SpeedyGA is a vectorized implementation of a genetic algorithm in the Matlab programming language. Without bells and whistles, it faithfully implements the specification for a Simple GA given on pgs 10, 11 of M. Mitchell's GA book. See comments in code for details.This script has played a crucial...


Platforms: Matlab

We use the genetic algorithm (gatool) to determine the three parameters of the simple Antoine equation and the six parameters of the Modified Antoine Model. Predictions are in perfect agreement with experimental data of vapor pressure of ethanol versus temperature for a temperature range from...


Platforms: Matlab