|
@@ -45,8 +45,25 @@
|
|
|
\section{Heuristics devlopped}
|
|
|
|
|
|
\subsection{Fully random search (Test case)}
|
|
|
- Blabla+pipeline
|
|
|
- \subsection{The Knuth-Fisher-Yates shuffle}
|
|
|
+ The first heuristic implemented is rge random search. We generates
|
|
|
+ random sets of Halton points and select the best set with regard to its
|
|
|
+ discrepancy iteratively. The process is wraped up in the following
|
|
|
+ flowchart~\ref{rand_flow}. In order to generate at each step a random
|
|
|
+ permutation, we transform it directly from the previous one.
|
|
|
+ More precisely the permutation is a singleton object which have method
|
|
|
+ random, built on the Knuth Fisher Yates shuffle. This algorithm allows
|
|
|
+ us to generate an uniformly chosen permutation at each step. We recall
|
|
|
+ this fact and detail the algorithm in the following section.
|
|
|
+\begin{figure}
|
|
|
+ \begin{mdframed}
|
|
|
+ \label{rand_flow}
|
|
|
+\includegraphics[scale=0.4]{flow_rand.pdf}
|
|
|
+\caption{Flowchart of the random search}
|
|
|
+\end{mdframed}
|
|
|
+\end{figure}
|
|
|
+
|
|
|
+
|
|
|
+ \subsubsection{The Knuth-Fisher-Yates shuffle}
|
|
|
|
|
|
The Fisher–Yates shuffle is an algorithm for generating a random permutation
|
|
|
of a finite sets. The Fisher–Yates shuffle is unbiased, so that every
|
|
@@ -86,6 +103,9 @@ the naive implementation.
|
|
|
algorithm is thus unbiaised.
|
|
|
|
|
|
\end{proof}
|
|
|
+
|
|
|
+
|
|
|
+\subsubsection{Results and stability}
|
|
|
\subsection{Local Search}
|
|
|
Blabla+Pipeline
|
|
|
\subsection{Evolutionary heuristics}
|