From: Florian Forster Date: Sat, 19 Feb 2011 17:24:17 +0000 (+0100) Subject: Erwähne die Arbeiten von Hillis und Juillé. X-Git-Url: https://git.verplant.org/?a=commitdiff_plain;h=fe805b66d32931f4f51d994a5dd122bd8663298a;p=diplomarbeit.git Erwähne die Arbeiten von Hillis und Juillé. --- diff --git a/diplomarbeit.tex b/diplomarbeit.tex index 7273cbe..c090c39 100644 --- a/diplomarbeit.tex +++ b/diplomarbeit.tex @@ -327,6 +327,47 @@ Sortiereigenschaft erhält. Transformationen von Sortiernetzwerken werden in Abschnitt~\ref{sect:tranformation} beschrieben, ein Algorithmus, der Mutation einsetzt, wird in Abschnitt~\ref{sect:sn-evolution-cut} vorgestellt. + +\begin{figure} + \begin{center} + \input{images/16-hillis.tex} + \end{center} + \caption{Das 16-Sortiernetzwerk, das \textit{Hillis} in~\cite{H1992} angibt. + Es besteht aus 61~Komparatoren in 11~Schichten.} + \label{fig:16-hillis} +\end{figure} +Evolutionäre Algorithmen wurden bereits mehrfach eingesetzt, um +Sortiernetzwerke zu untersuchen. \textit{W.~Daniel Hillis} verwendete +\emph{Co-Evolution} um neben Komparatornetzwerken auch „schwierige Eingaben“ +zu optimieren~\cite{H1992}. Diese \emph{Parasiten} genannten Eingaben wurden +daran gemessen, bei wievielen Komparatornetzwerken sie beweisen konnten, dass +sie keine Sortiernetzwerke sind. So mussten bei neuen Individuen~/ +Komparatornetzwerken nicht alle 0-1-Folgen, sondern nur erfolgreiche +Parasiten~/ schwierige Eingaben überprüft werden. Auf diese Art und Weise +gelang es \textit{Hillis} ein 16-Sortiernetzwerk mit 61~Komparatoren +anzugeben, das in Abbildung~\ref{fig:16-hillis} zu sehen ist. + +\begin{figure} + \centering + \subfigure{\input{images/13-juille-0.tex}} + \subfigure{\input{images/13-juille-1.tex}} + \caption{13-Sortiernetzwerke, die von \textit{Hugues Juillé} mithilfe des + END-Algorithmus gefunden wurden. Sie bestehen jeweils aus 45~Komparatoren in + 10~Schichten.} + \label{fig:13-juille} +\end{figure} +\textit{Hugues Juillé} entwickelte ein Verfahren, das er \emph{Evolving +Non-Determinism} (END) nannte. Dabei handelt es sich nicht um einen +\emph{Evolutionären Algorithmus}, wie sie hier vorgestellt wurden, sondern um +eine verteilte, probabilistische Breitensuche, die an die \emph{Strahlsuche} +(englisch: \textit{beam search}), ein Verfahren der Künstlichen Intelligenz, +angelehnt ist. Die aufwendigste Operation bei diesem Ansatz ist die +Bewertungsfunktion, die abschätzt, wieviele Komparatoren zu einem +Komparatornetzwerk hinzugefügt werden müssen, um ein Sortiernetzwerk zu +erhalten. Mit diesem Ansatz gelang es \textit{Juillé} zwei 13-Sortiernetzwerke +anzugeben, die mit 45~Komparatoren effizienter sind als alle bis dahin +Bekannten (Abbildung~\ref{fig:13-juille}). + \newpage \section{Bekannte konstruktive Sortiernetzwerke} \label{sect:konstruktive_netzwerke} diff --git a/images/13-juille-0.tex b/images/13-juille-0.tex new file mode 100644 index 0000000..fcdb05d --- /dev/null +++ b/images/13-juille-0.tex @@ -0,0 +1,195 @@ +\begin{tikzpicture}[auto] +\node[vertex] (v0) at (0.47,0.00) {}; +\node[vertex] (v1) at (0.47,1.50) {}; +\path[comp] (v0) -- (v1); + +\node[vertex] (v2) at (0.61,0.38) {}; +\node[vertex] (v3) at (0.61,2.26) {}; +\path[comp] (v2) -- (v3); + +\node[vertex] (v4) at (0.75,1.13) {}; +\node[vertex] (v5) at (0.75,3.38) {}; +\path[comp] (v4) -- (v5); + +\node[vertex] (v6) at (0.47,1.88) {}; +\node[vertex] (v7) at (0.47,4.13) {}; +\path[comp] (v6) -- (v7); + +\node[vertex] (v8) at (0.61,2.63) {}; +\node[vertex] (v9) at (0.61,4.51) {}; +\path[comp] (v8) -- (v9); 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