optiGA - Genetic Algorithms ActiveX Control 2.0.1
optiGA for VB is an ActiveX control (OCX) for the implementation of Genetic Algorithms (GA). No matter what is the nature of your optimization problem might be, optiGA is a generic control that will perform the genetic run for you. With very little coding needed, you can be up and running in no time. Just define your variables (binary, real or integers), code the fitness function and you are set to go. On the other hand, you can override...
Price: USD $125.00;
License: Shareware (Free to Try)
Platform: Windows 95, Windows, Windows NT, Windows 2000, Windows XP, Windows 98, Windows Me
Genetic System Component (ActiveX) 1.0 Genetic System can be used to solve optimization problems. This component can be used to construct binary genetic system, it provides user with appropriate functionality to control the generation.Following features are provided:Unlimited population size Unlimited string length Following factors can be controlled (Crossover Probability, Mutation, Rolette wheel) Optimized for fast processing Ability to save and load ActiveX component can be...
Price: USD $49.00;
License: Shareware (Free to Try)
File Size: 1916 KB;
Platform: Windows Me, Windows, Windows 2000, Windows XP, Windows 2003
Popgene - Population Genetic Analysis 1.32
Analyze genetic variation with this tool. Popgene - Population Genetic Analysis help you with the analysis of genetic variation among and within populations using co-dominant and dominant markers.This software makes population genetics analysis more accessible for the casual computer user and more handy for the experienced computer user.Simple menus and dialog box selections enable you to perform complex analyses and produce scientifically...
Genetic Code 1.0
Study the genetic code with this tool. Genetic Code can display the universal genetic code, the one- and three-letter amino acid codes, the biochemical structures of the amino acids, and amino-acid similarity (Dayhoff matrix).
Desktop Genetic Algorithms PreAlpha
Desktop Genetic Algorithms is a software for analyzing and calculating genetic algorithms. Mainly include the codes of genetic algorithm, interative genetic algorithm, that are written in Java Applcations also included such as function optimization, simple fashion design optimization, face optimization and so onRequirements:* Java
Galileo: a Distributed Genetic Algorithm 1.0
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
Genetic Algorithms Framework 0.7.0
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.
Genetic Programming Framework 3.5.0
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.
JAGA - Java API for Genetic Algorithms 1.0.b.13.04.04
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.
Java Genetic Algorithm Library 0.8.1.0
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.
Java rapid genetic programming 101
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.
Python Neural Genetic Algorithm Hybrids 0.3.0
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.