Free genetic algorithm software for windows

Genetic Algorithm Viewer was designed to show the functioning of a genetic algorithm.
Genetic Algorithm Viewer permits the user to test the major parameters of a genetic algorithm.
Physics, Biology, Economy or Sociology often have to deal with the classical problem of optimization. Economy particularly has become specialist of that field1. Generally speaking, a large part of mathematical development during the XVIIIth century dealt with that topic (remember those always repeated problems where you had to obtain the derivative of a function to find its extremes).
Purely analytical methods widely proved their efficiency. They nevertheless suffer from a insurmountable weakness : Reality rarely obeys to those wonderful differentiable functions your professors used to show you2.
Other methods, combining mathematical analysis and random search have appeared. Imagine you scatter small robots in a Mountainous landscape.
Those robots can follow the steepest path they found. When a robot reaches a peak, it claims that it has found the optimum. This method is very efficient, but theres no proof that the optimum has been found, each robot can be blocked in a local optimum. This type of method only works with reduced search spaces.

Genotype Calculator 1.01 is designed as a free program that simulates the genetic makeup of an F1 generation based on the alleles of two parents. Useful for quickly simulating Punnett Squares (even up to 20 alleles per parent).

Enhancements: Fixes some bugs and adds the calculation time to the data displayed.

Ease working with classical and state-of-the-art optimization algorithms on standard benchmark problem domains The Optimization Algorithm Toolkit (OAT) is a workbench and toolkit for developing, evaluating, experimenting, and playing with classical and state-of-the-art optimization algorithms on standard benchmark problem domains; including reference algorithm implementations (Java), graphing, visualizations and much more.
Main features:
- Graphical User Interface (GUI) with graphs, configuration dialogs, visualizations and much more for the research scientist and interested novice alike.
- Fully featured Application Programming Interface (API) for experimentation, implementation of additional algorithms and problem domains.
- Source code is included to help you learn more about how optimization algorithms work, and to contribute new algorithms and problems to the project.
- Example code to get you up and running quickly and unit tests to ensure algorithm robustness and consistency.
- Provides a focus on computational intelligence and biologically inspired optimization algorithms such as evolutionary algorithms, artificial immune system algorithms, particle swarm optimization, ant colony optimization, and much more.
- Provides literature references for all algorithm implementations (See a full algorithm listing with references).
- Problem Domains Include:
- Continuous Function Optimization, including 24 Algorithms not limited to Evolutionary Algorithms, Artificial Immune System Algorithms, Physics Algorithms, Swarm Algorithms, and Random Algorithms. 69 problem instances not limited to classic functions such as De Jongs F-series, Ackley, Easom, Goldstein, Griewangk, Langermann, Michalewicz, Rastrigin, Rosenbrocks Valley, Schwefel, Six Hump Camel Back, Three Pot Holes, Mahfouds M-series, Bohachevsky, Hansen, Shubert, and many others.
- Huygens Search & Optimization Benchmark Suite: all function optimization algorithms, billions of fractal optimization instances, benchmark server, and much more
- Traveling Salesman Problem (TSP), including 10 algorithms not limited to Ant Colony Optimization, Evolutionary Algorithms, Artificial Immune System Algorithms, and Random Algorithms. 15 problem instances from TSPLib including Berlin52, Burma14, Ulysses22, and many others.
- Protein Folding (Protein Structure Prediction - PSP), including 3 algorithms including Evolutionary Algorithms, and Random Algorithms. 14 problem instances all examples of Dills Hydrophobic-Polar (HP) Model
- Binary Optimization Functions including 6 algorithms not limited to Evolutionary Algorithms, Bit Hill-Climbers, Artificial Immune System Algorithms, and Random Algorithms. 10 problem instances not limited to Trap Functions, Deceptive Functions, Massively Multimodal, OneMax, and others.
- Graph Coloring Problem (GCP), including 2 algorithms including an Artificial Immune System algorithm, 56 problem instances all benchmark instances from the GCP repository on Michael Tricks Operations Research page
- Binary Character Recognition (BCR): 2 algorithms, 1 problem instance (lippman binary character recognition)
System requirements:
- Java

Enables files and folders to be encrypted into one big bank file. Icons, filenames and the files are encrypted using the Blowfish encryption algorithm.