| Travelling Salesman Problem |
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An applet showing a solution of the Traveling Salesman Problem using a GA.
Submitted: Nov 21, 1999
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| Genetic Java |
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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 parameters while the GA is running to see the real-time effects.
Submitted: Dec 10, 1999
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| TSP |
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This is a demonstration of the Travelling Salesman Problem (TSP). The applet uses a genetic algorithm.
Submitted: Dec 10, 1999
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| GA Example (1D - Minimum of function) |
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As you already know from the chapter about search space, problem solving can often be expressed as looking for the extreme of a function. This is exactly what the problem shown here is. Some function is given and the GA tries to find the minimum of the function. For other problems we just have to define a search space and the fitness function which means to define the function, which we want to find the extreme for.
Submitted: Nov 21, 1999
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| The GA Playground |
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The GA Playground is a general purpose genetic algorithm toolkit where the user can define and run his own optimization problems. The toolkit is implemented in the Java language, and requires (when used as an application, in its full mode), a Java compiler and a very basic programming knowledge (just enough for coding a fitness function). Defining a problem consists of creating an Ascii definition file in a format similar to Windows Ini files, and modifying the fitness function in the GaaFunction source file. In addition, other methods can (optionally) be overwritten (e.g. the drawing method), other classes can be extended or replaced, and additional input can be supplied through Ascii files.
Submitted: Nov 22, 1999
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| Java-Solitaire: Play or find solutions with a genetic algorithm! |
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With this applet you can play Peg-solitaire on one hand: Open a solitaire-window with the button play your game and use your mouse and the buttons to move, go back and forth through the history of moves or let rerun the whole game. On the other hand, you can let the applet search a solution. This is not a built-in, predefined solution, but sought in real-time; it takes some minutes (As soon as the applets download to your browser has completed, you may cut the line to your provider, as the connection isn't required any longer).
Submitted: Dec 15, 1999
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| Vehicle Routing Problem with Time Windows |
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An applet showing a solution of the Vehicle Routing Problem with Time Windows using a GA. Demo of a JAVA based library to perform tour and resource optimization tasks. Based upon genetic algorithms for different industries.
Submitted: Mar 21, 2006
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| Genetic Drift |
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Simple Java simulations of basic population genetic processes.
Submitted: Dec 10, 1999
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| Biomorph |
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Biomorph is an educational applet that aims to teach the basic principles of evolution: random mutation followed by non-random selection. It is derived from an algorithm described by Professor Richard Dawkins in Artificial Life and The Blind Watchmaker.
Submitted: Dec 10, 1999
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| Java/C Symbolic Regression Applet |
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A lean implementation of Symbolic
Regression in C (sourcecode available) and as a Java Applet.
The resulting equations of the Symbolic Regression Applet are printed
in a format compatible with the most spreadsheet-applications.
Submitted: Oct 01, 2004
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| n-genes |
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N-genes is an evolutionary computing framework for Java5. It focuses on usability and performances. Genetic programing is implemented through high-level stack-based language.
Submitted: Nov 19, 2005
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| Java Demonstration of the Synchronization Task |
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The applet demonstrates a cellular automaton (CA) evolved to solve the synchronization task. In this task, the one-dimensional, binary-state CA, upon given any initial configuration, must reach a final configuration, within a given number of time steps, that oscillates between all 0s and all 1s on successive time steps. The CA demonstrated was evolved via the cellular programming evolutionary algorithm.
Submitted: Dec 10, 1999
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