thesis: aded abstract and kurzfassung; re-read conclusion and evaluation to iron out mistakes etc.

thesis/chapters/conceptdesign.tex

@@ -35,11 +35,11 @@ The prototypes developed in this thesis, are part of a GP algorithm for symbolic
 
 The parameters themselves are unique to each expression, meaning they have a one-to-one mapping to an expression. Furthermore, as can be seen in Figure \ref{fig:input_output_explanation}, each expression can have a different number of parameters, or even no parameters at all. However, with no parameters, it wouldn't be possible to perform parameter optimisation. This is in contrast to variables, where each expression must have the same number of variables. Because parameters are unique to each expression and can vary in size, they are not structured as a matrix, but as a vector of vectors.
 
-An important thing to consider, is the volume and volatility of the data itself. The example used above has been drastically simplified. It is expected, that there are hundreds of expressions evaluate per GP generation. Each of these expressions may contain between ten and 50 tokens. A token is equivalent to either a variable, a parameter, a constant value or an operator.
+An important thing to consider, is the volume and volatility of the data itself. The example shown in Figure \ref{fig:input_output_explanation} has been drastically simplified. It is expected, that there are hundreds of expressions evaluate per GP generation. Each of these expressions may contain between ten and 50 tokens. A token is equivalent to either a variable, a parameter, a constant value or an operator.
 
-Usually, the number of variables per expression is around ten. However, the number of variable sets can increase drastically. It can be considered, that $1\,000$ variable sets is the lower limit. On the other hand, $100\,000$ can be considered as the upper limit. Considering that one variable takes up 4 bytes of space and 10 variables are needed per expression, at least $4 * 10 * 1\,000 = 40\,000$ bytes and at most $4 * 10 * 100\,000 = 400\,000$ bytes need to be transferred to the GPU for the variables.
+It can be assumed that typically the number of variables per expression is around ten. However, the number of variable sets can increase drastically. It can be considered, that $1\,000$ variable sets is the lower limit. On the other hand, $100\,000$ can be considered as the upper limit. Considering that one variable takes up 4 bytes of memory and 10 variables are needed per expression, at least $4 * 10 * 1\,000 = 40\,000$ bytes and at most $4 * 10 * 100\,000 = 400\,000$ bytes need to be transferred to the GPU for the variables.
 
-These variables do not change during the runtime of the symbolic regression algorithm. As a result the data only needs to be sent to the GPU once. This means that the impact of this data transfer is minimal. On the other hand, the data for the parameters is much more volatile. As explained above, they are used for parameter optimisation and therefore vary from evaluation to evaluation and need to be sent to the GPU very frequently. However, the amount of data that needs to be sent is also much smaller. TODO: ONCE I GET THE DATA SEE HOW MANY BYTES PARAMETERS TAKE ON AVERAGE
+These variables do not change during the runtime of the symbolic regression algorithm. As a result the data only needs to be sent to the GPU once. This means that the impact of this data transfer is minimal. On the other hand, the data for the parameters is much more volatile. As explained above, they are used for parameter optimisation and therefore vary from evaluation to evaluation and need to be sent to the GPU very frequently. The amount of data that needs to be sent depends on the number of expressions as well as on the number of parameters per expression. Considering $10\,000$ expressions that need to be evaluated and an average of two parameters per expression each requiring 4 bytes of memory, a total of $10\,000 * 2 * 4 = 80\,000$ bytes need to be transferred to the GPU on each parameter optimisation step.
 
 \section{Architecture}
 \label{sec:architecture}