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% Title Page
\title{\textbf{COSC 4P82 Assignment 1}}
\author{\textbf{Brett Terpstra}\\
	bt19ex@brocku.ca - 692021}

\begin{document}
\maketitle
\tableofcontents

\section{Introduction}

\section{Symbolic regression}
\subsection{Introduction}
\subsection{Parameter Table}
\begin{center}
\begin{tabularx}{0.8\textwidth}{ | >{\centering\arraybackslash}X | >{\centering\arraybackslash}X | }
	\hline
	Parameter & Value \\ [0.25ex]
	\hline\hline
	Runs & 10 \\
	\hline
	Population Size & 5000 \\
	\hline
	Generations & 50 \\
	\hline
	Training Set & N/A \\
	\hline
	Testing Set & N/A \\
	\hline
	Crossover Operator & Subtree Crossover\\
	\hline
	Mutation Operator & Grow Tree, Max Depth 4 \\
	\hline
	Crossover Rate & 0.9 or 1.0* \\
	\hline
	Mutation Rate & 0.1 or 1.0* \\
	\hline
	Elitism & Best 2 or 0 individuals Survive* \\
	\hline
	Selection & Fitness Proportionate \\
	\hline
	Function Set & *, /, +, -, exp, log, sin, cos \\
	\hline
	Terminal Set & X, Ephemeral Value \\
	\hline
	Tree Initialization & Half and Half, Max Depth 2-6 \\
	\hline
	Max Tree Depth & 17 \\
	\hline
	Raw Fitness & See Fitness Evaluation \\
	\hline
	Standardized Fitness & = Raw Fitness \\
	\hline
\end{tabularx}
\end{center}
*4 Tests were run, 0.9 crossover, 0.9 mutation with 0 elitism and 2 elitism, and 1.0 crossover, 1.0 mutation with 0 elitism and 2 elitism.
\subsection{Fitness Evaluation}
Fitness is evaluated by taking the absolute value of the predicted y value minus the actual y value. 
If the difference is less than a user provided (default 1.e15) value cutoff it is added to the fitness value. If the difference value is less than the float epsilon value (\~= 0) the number of hits is incremented. Lower fitness values are preferred.  
\subsection{Fitness Plots}
\begin{figure}[H]
	\centering
	\includegraphics[width=1.0\linewidth]{fp5}
	\caption{2 Elites, 10 Runs Averaged}
	\label{fig:fp4}
\end{figure}
\begin{figure}[H]
	\centering
	\includegraphics[width=1.0\linewidth]{fp3}
	\caption[]{0 Elites, 10 Runs Averaged}
	\label{fig:fp2}
\end{figure}
\subsection{Analysis And Conclusion}
The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.1 mutation. 



\section{Rice Classification}
\subsection{Introduction}
\subsection{Parameter Table}
\begin{center}
	\begin{tabularx}{0.8\textwidth}{ | >{\centering\arraybackslash}X | >{\centering\arraybackslash}X | }
		\hline
		Parameter & Value \\ [0.25ex]
		\hline\hline
		Runs & 10 \\
		\hline
		Population Size & 5000 \\
		\hline
		Generations & 50 \\
		\hline
		Training Set & N/A \\
		\hline
		Testing Set & N/A \\
		\hline
		Crossover Operator & Subtree Crossover\\
		\hline
		Mutation Operator & Grow Tree, Max Depth 4 \\
		\hline
		Crossover Rate & 0.9 or 1.0* \\
		\hline
		Mutation Rate & 0.1 or 1.0* \\
		\hline
		Elitism & Best 2 or 0 individuals Survive* \\
		\hline
		Selection & Fitness Proportionate \\
		\hline
		Function Set & *, /, +, -, exp, log, sin, cos \\
		\hline
		Terminal Set & X, Ephemeral Value \\
		\hline
		Tree Initialization & Half and Half, Max Depth 2-6 \\
		\hline
		Max Tree Depth & 17 \\
		\hline
		Raw Fitness & See Fitness Evaluation \\
		\hline
		Standardized Fitness & = Raw Fitness \\
		\hline
	\end{tabularx}
\end{center}

\section{Compiling / Executing}



\end{document}          
