This thesis focuses on two improvements to that methodology,
1) using three requirements models instead of one
and 2) using a simpler discretization policy
that improves the performance of the method.
The orignal work used the \holo model,
this thesis re-uses \holo, as well as two additional
models, called \aero and \cob
(see \tion{ddpDes} for a description of these models).
Increasing the number of models
decreases the risk that ITL only worked by
exploiting some pecularity of the orignal model.
The objective function used in the orignal work
was the normalized benefit/cost ratio.
But since the treatment learner must have discrete target classes,
the benefit-cost plane was striped to create multiple target classes.
%In chapter \ref{chap:itl} 
This thesis investigates the use of {\it extreme sampling}
(defined in \tion{xsampling}).
Extreme sampling takes the approach that only
two classes are necessary,
instances that are {\it good} and those that are {\it bad}.
Using extreme sampling decreases
the number of instances needed by ITL
and improves the quality of the solution
found.

The orignal work on ITL also only placed this technique
in the context of machine learning algorithms.
In this thesis we explore placing
ITL in the context of metaheuristic search.
To put ITL in this new context metaheuristic search will be discussed
in \tion{metaheur}, search based software engineering will
be discuess in \tion{sbse}, and the search characteristics
of ITL will be discussed in \tion{itlOver}.

This thesis address addresses these SPY issues in the models
studied in \chap{others} and \chap{properties}.
