Tutorial - Symbolic Regression.

1. invoke 'gool' from the main 'jrgp' directory, specifying the project 'examples/symbolic_regression' as option at command line;
2. open the 'prj directory' subtree and select in the 'symbolic_regression.Regr' node [corresponding to Regr.class whose source file is Regr.java in the same dir] all the methods but the 'cut' one (it is an helper function); use the context popup option 'to gp-function' to extract them as GPFunction subclasses;
3. select the newly created java classes and copy them in the 'function pool' node, via the popup options [copy&paste] or the key shortcuts;
4. open the 'parameters factory' and copy the functions nodes from the 'function pool' into the 'function set';
5. use the 'arguments' node 'add arguments...' option to add one 'NO_TYPE' argument: the programs take in this application one argument corresponding to the x evaluation point;
6. set the 'evaluator' node with the 'set...' popup option to the file 'RegrEvaluator.class' [source file RegrEvaluator.java in the same dir];
7. create a population of 1000 individuals by selecting 'create population' from the 'parameters factory' popup;
8. you can now modify the population parameters from the '_population0/parameters' node. Turn for example the overselection option 'ON'; parameters changes become effective through 'force changes' popup option or at next generation run;
9. run the population for one or more generations with the corresponding options in the population node;
10. try now to remove the 'FConst' function from the 'parameters/function set' subtree [delete popup option] and then run the population again.

Note: you can quit gool using the 'exit' popup option of 'gap' node.