Introduction: slight improvements

thesis/chapters/introduction.tex

@@ -7,7 +7,7 @@ This chapter provides an entry point for this thesis. First the motivation of ex
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-Optimisation and acceleration of program code is a crucial part in many different fields. For example video games need optimisation to lower the minimum hardware requirements which allows more people to run the game. Another example where optimisation is important are computer simulations. For those, optimisation is even more crucial, as this allows the scientists to run more detailed simulations or get the simulation results faster. Equation learning is another field that can heavily benefit from optimisation. One part of equation learning, is to evaluate the expressions generated by the algorithm which can make up a significant portion of the runtime of the algorithm. This thesis is concerned with optimising the evaluation part to increase the overall performance of the equation learning algorithm.
+Optimisation and acceleration of program code is a crucial part in many fields. For example video games need optimisation to lower the minimum hardware requirements which allows more people to run the game, increasing sales. Another example where optimisation is important are computer simulations. For those, optimisation is even more crucial, as this allows the scientists to run more detailed simulations or get the simulation results faster. Equation learning is another field that can heavily benefit from optimisation. One part of equation learning, is to evaluate the expressions generated by the algorithm which can make up a significant portion of the runtime of the algorithm. This thesis is concerned with optimising the evaluation part to increase the overall performance of the equation learning algorithm.
 
 Considering the following expression $x_1 + 5 - \text{abs}(p_1) * \text{sqrt}(x_2) / 10 + 2 \char`^ 3$ which contains simple mathematical operations as well as variables $x_n$ and parameters $p_n$. This expression is one example that can be generated by the equation learning algorithm and needs to be evaluated for the next iteration. Usually multiple expressions are generated per iteration, which also need to be evaluated. Additionally, multiple different values need to be inserted for all variables and parameters, drastically increasing the amount of evaluations that need to be performed.