| Alife Database By Html (Main Menu) |
| FSA-GA Ants | Finite State Automata -- GA Ants | Ants is a program to explore genetic programming and learning. When Ants starts, you'll see a large green "food area" in the center of the screen. Across the top of the screen, will be some "ants" aimlessly milling about. In the title bar is the pro |
| Genetic Java!!!! | Sample Genetic Algorithm Applet: | This applet is designed to be a tool by which interested people can learn some of the basics of genetic algorithms (GAs) while being able to experiment with a real GA. Users can select values for the GA's search parameters and can modify the paramete |
| Manna Mouse | Food, Bugs, Evolution | Among interactive artificial life simulations, Manna Mouse is uniquely simple: A creature's genome is only an encoding of its position on the screen. With your Mouse you paint Manna--the reward function. By visualizing the fitness landscape, and you |
| Travelling Salesman | TSP interactive applet | This program utilises a genetic algorithm to find solutions for the Travelling Salesman Problem. Briefly, a random route is created, mutated offspring routes are produced, and the best of these (in terms of total length of route) is selected as the n |
| Truck Demo | A GP Interactive Truck Applet | A GP Interactive Truck Applet |
| Bugs in Delphi | Evolving bugs in Delphi | The program simulates the history and life of a small population of small "bugs" that move according to a simple genetic code |
| eFloys - Evolving Floys | Floys evolving by Genetic Algorithm | eFloys (evolving Floys), are social, territorial, evolving artificial life creatures, implemented in Java. They belong to the flocking Alife creatures variety, sharing with them the social tendency to stick together, and the lifelike emergent behavio |
| A Fast TSP Solver | A Fast TSP Solver using GA | This JAVA applet is based on the algorithm proposed in `A Fast TSP Solution using Genetic Algorithm' (Information Processing Society of Japan 46th Nat'l Conv., 1993). |
| Genetic Algorithms | GA Tutorial | The following is an updated excerpt from my books Genetic Algorithms in C++ (M&T Books, 1995) and Active Visual J++ (Microsoft Press, 1997). Note: This page is long. You may want to print or download it for reading offline. |
| Optimization by Evolution | A Java TSP applet | TSPbyGA is a Java applet that lets you experiment with optimizing a Traveling Salesman (TSP problem using a genetic algorithm. |
| GA - Maze Solver | A configurable GA Java maze solver | Maze solver is a configurable genetic algorithm It does not find the optimal path,but usually finds a path to the target. |
| GA Commercial Software | a general purpose package for Excel | Genetic Algorithm Software Products: GENERATOR a general purpose package for solving a variety of problems. GENERATOR-TFV a financial version of Generator. CHEMSOLVER a version of Generator for solving problems in chemistry. Downloadable Sample Probl |
| Genetic Algorithms Software | Catalog of GA software (various platforms) | Catalog of GA software (various platforms) |
| TSP Using EP | TSP Using Evolutionary Programming | This is an applet I decided to write after seeing how evolutionary programming could be used to solve hard problems. This applet works by taking the best city path and multiplying it by the population size. In the process the strings are mutated to |
| Gamelan GA | Gamelan GA Java Resources | Gamelan GA Java Resources |
| Echo | GA-oriented ecology system | GA-oriented ecology system |
| TSP Applet | The travelling salesman problem | The travelling salesman problem |
| Biomorph | Interactive Biomorph Applet | Interactive Biomorph Applet |
| Echo | GA-oriented ecology system | Echo is a simulation tool developed to investigate mechanisms which regulate diversity and information-processing in systems comprised of many interacting adaptive agents, or complex adaptive systems (CAS). Echo agents interact via combat, mating and |
| Memetic Algorithms | Memetic Algorithms' Home Page | Memetic Algorithms is a population-based approach for heuristic search in optimization problems. They have shown that they are orders of magnitude faster than traditional Genetic Algorithms for some problem domains. Basically, they combine local sear |
Ariel Dolan