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Added Graphics about Single-Point vs. Runtime Behavior
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part_install.tex

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\begin{itemize}%
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\item install Docker from\\\url{http://www.docker.com/} and do%
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\item \mbox{\codeil{docker run -t -i -p 9999:8080/tcp}} \mbox{\codeil{optimizationbenchmarking/evaluator-gui}}%
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\item open browser to \codeil{http://localhost:9999}%
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\item open browser to \codeil{http://localhost:9999} on Linux, replace \codeil{localhost} with the IP address of the Docker container under Windows or Mac OS%
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\end{itemize}%
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%
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\vfill%
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}%
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%
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\locateGraphic{2-}{width=0.33\paperwidth}{graphics/website/website}{0.63}{0.47}%
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\locateGraphic{2-}{width=0.33\paperwidth}{graphics/website/website}{0.63}{0.45}%
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\locateGraphic{1}{width=0.33\paperwidth}{graphics/barcodes/website}{0.63}{0.47}%
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%
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\end{itemize}%

part_introduction.tex

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@@ -5,11 +5,11 @@ \section{Introduction}%
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\frametitle{Quick Overview}%
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\begin{itemize}%
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\item Concept of optimization algorithms%
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\item<2-> How to benchmark such algorithms%
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\item<2-> How to benchmark optimization algorithms%
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\item<3-> How to evaluate data obtained from benchmarking and how to compare algorithms
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\item<4-> The \optimizationBenchmarking\ Framework can do it for you\only<-4>{!}\only<5->{:}%
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\item<5-> It provides a graphical user interface in a client/server application for loading, editing, and evaluating experimental results.%
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\item<6-> It can run as Docker container under Linux, MacOS?, and Windows? without needing any additional software (except Docker and a browser).%
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\item<6-> It can run as Docker container under Linux, MacOS, and Windows without needing any additional software (except Docker and a browser).%
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\item<7-> It produces reports, similar to articles, in \LaTeX\ with figures and building blocks ready for use in your publications%
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\end{itemize}%
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\end{frame}%
@@ -87,32 +87,38 @@ \section{Introduction}%
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\end{itemize}%
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}%
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%
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\item<10-> \alert{Experimental analysis and comparison only practical alternative.}%
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\item<10-> \alert{Experimental analysis and comparison is the only practical alternative.}%
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%
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\end{itemize}%
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\end{frame}%
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%
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%
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%
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\begin{frame}%
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\begin{frame}[t]%
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\frametitle{Performance and Anytime Algorithms}%
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%
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\emph{\inQuotes{We use metaheuristic optimization algorithms to give us \alert<3->{good approximate solutions} within \alert<4->{acceptable runtime}.}}%
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%
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\uncover<2->{%
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\begin{itemize}%
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\item Algorithm performance has two dimensions\scitep{NAFR2010RPBBOB2ES,WCTLTCMY2014BOAAOSFFTTSP}:\uncover<3->{ solution quality\uncover<4->{ and required runtime}}%
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\only<-9>{%
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\item<5-> Anytime Algorithms\scitep{BD1989STDPP2} are optimization methods which maintain an approximate solution at \emph{any time} during their run and iteratively improve this guess.%
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\item<6-> All metaheuristics are Anytime Algorithms.%
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\item<7-> Several exact methods like Branch-and-Bound\scitep{LMSK1963AAFTTSP,Z1993TBABACSOTATSP,Z1999TAADFBABACSOTATSP} are Anytime Algorithms.%
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\item<8-> Consequence: Most optimization algorithms produce approximate solutions of different qualities at different points during their process.%
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\item<9-> Experiments must capture solution quality and runtime data.%
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}%
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\item<10-> If we just compare \inQuotes{final} results, we may arrive at incomplete \only<12->{or entirely wrong} conclusions%
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\end{itemize}%
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}%
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%
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\locateGraphic{3}{width=0.55\paperwidth}{graphics/performance/performance_dimensions/performance_dimensions_1}{0.225}{0.542}%
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\locateGraphic{4}{width=0.55\paperwidth}{graphics/performance/performance_dimensions/performance_dimensions_2}{0.225}{0.542}%
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\locateGraphic{5}{width=0.55\paperwidth}{graphics/performance/performance_dimensions/performance_dimensions}{0.225}{0.542}%
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\locateGraphic{5,10}{width=0.55\paperwidth}{graphics/performance/performance_dimensions/performance_dimensions}{0.225}{0.542}%
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\locateGraphic{11}{width=0.55\paperwidth}{graphics/performance/performance_dimensions/performance_cuts}{0.225}{0.542}%
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\locateGraphic{12}{width=0.55\paperwidth}{graphics/problems_with_points/points}{0.225}{0.542}%
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\locateGraphic{13}{width=0.55\paperwidth}{graphics/problems_with_points/lines}{0.225}{0.542}%
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%
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\end{frame}%
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%

part_maxSatExample.tex

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\begin{itemize}%
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\item We want to compare the performance of six algorithms\uncover<2->{:%
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\begin{enumerate}%
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\item 1-flip Hill Climber%
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\item<3-> 1-flip Hill Climber with Restarts%
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\item 1-flip Hill Climber\only<-2>{: starts with a random bit string, flips one of the \maxSatVariables\ bits in each iteration and keeps the new bit string if it is better}%
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\item<3-> 1-flip Hill Climber with Restarts\only<3>{ after $z$ moves without improvements; initially $z=1$ and increased by~$1$ after each restart}%
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\item<4-> 2-flip Hill Climber%
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\item<5-> 2-flip Hill Climber with Restarts
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\item<6-> $m$-flip Hill Climber
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\item<5-> 2-flip Hill Climber with Restarts%
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\item<6-> $m$-flip Hill Climber\only<6>{, $m$ chosen randomly according to geometric distribution}%
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\item<7-> $m$-flip Hill Climber with Restarts%
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%
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\end{enumerate}%
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\item<5-> The problem instances have the following features\uncover<6->{:%
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\begin{itemize}%
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\item \maxSatVariables: the number of variables%
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\item<7-> \maxSatClauses: the number of clauses (related to \maxSatVariables)%
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\item<7-> \maxSatClauses: the number of clauses (here related to \maxSatVariables)%
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\end{itemize}%
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}%
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\end{itemize}%
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%}%
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%
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%\only<6-7>{%
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\item<6-> We specify which benchmark instances we have and what their features are\uncover<7->{: $11\times 11$ instances in our example, with features \maxSatVariables\ and \maxSatClauses\uncover<-7>{ (\alert{demo})}}%
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\item<6-> We specify which benchmark instances we have and what their features are\uncover<7->{: $10\times 10$ instances in our example, with features \maxSatVariables\ and \maxSatClauses\uncover<-7>{ (\alert{demo})}}%
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%}%
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%
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%\only<8-9>{%

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