EO - The Algorithm-Based approach

Congratulations - You have chosen the algorithm-based approach!  This means that you want to start from something that already works, and gradually learn about the more complex constructs. We have prepared a series of "lessons" for you.

• Lesson 1 - a gentle introduction to the EO way: your first steps into EO representations using a simple generational GA. Please, spend the necessary time on that one, since all basic constructs presented there are used throughout EO.
• Lesson 2 - encapsulate, encapsulate, and try more sophisticated selection/replacement mechanisms, as well as multiple  operators
• Lesson 3 - The same algorithms, but with improved input/outputs: user-friendly input (i.e. without the need to recompile!) of algorithm parameters, and checkpointing (display of on-line statistics, save and restore populations, restart stopped runs, ...).


Current version (Jan. 5, 2001) stops here, but ...

From there on, you don't need to follow the lesson order any more: you can go directly to any lesson and experiment. Of course, you will need to bring together the different pieces to write exactly what you want - but only because we had no idea of what you exactly want :-)
 

• Lesson 4 - More about checkpointing: write your first adaptive mechanism, and find out how easy it is to update and monitor dynamic parameters
• Lesson 5 - More general operators: e.g. binary, n-ary, or even specific mate selection (your brain and my beauty)!
• Lesson 6 - Why not go parallel? From the simple asynchronous SSGA to the more sophisticated island model (no totally distributed population yet).
• Lesson 7 - Use your own representation. You need only to write the base individual class, the initilization class and the variation operators. EO provides many helps: The eoExternal paradigm let you encapsulate into EO classes existing code. And the eoFixedLength and eoVariableLength super classes allow quick definition of new genotypes that handle identical Atoms.