Changeset 51


Ignore:
Timestamp:
Dec 19, 2009, 6:03:12 PM (15 years ago)
Author:
Rick van der Zwet
Message:

All completed versions

Location:
liacs/nc
Files:
3 added
3 edited

Legend:

Unmodified
Added
Removed
  • liacs/nc/low-correlation/Makefile

    r42 r51  
    22CFLAGS=-lm
    33
    4 SOURCE=autocorrelation.m.tex initseq.m.tex  mcs-call.m.tex \
    5        mcs.m.tex  mutation.m.tex  randint.m.tex  sa-call.m.tex  sa.m.tex
     4SOURCE=$(patsubst %.m,%.m.tex, $(shell ls *.m))
    65
    76include latex.mk
     
    98mcs: mcs.m
    109        @octave --silent mcs.m
    11 
    12 
    13 %.m.tex: %.m
    14         highlight --linenumbers --no-doc --latex --input $< --output $@
  • liacs/nc/low-correlation/latex.mk

    r45 r51  
    5656.gs.png:
    5757        $(GNUPLOT) $?
     58
     59%.m.tex: %.m
     60        highlight --linenumbers --no-doc --latex --style-outfile highlight.sty \
     61          --style vim --wrap --line-length 80 --line-number-length 3 \
     62          --input $< --output $@
  • liacs/nc/low-correlation/report.tex

    r46 r51  
    1818\usepackage{color}
    1919\usepackage{subfig}
     20\usepackage{marvosym}
    2021\floatstyle{ruled}
    2122\newfloat{result}{thp}{lop}
     
    5657
    5758\section{Problem description}
    58 Due to the huge 'exploding' posibilities it is not possible to walk trough the
    59 whole list of posibities, so we need alternative approches to tackle this
    60 problem. First we will try the \emph{Monte Carlo Search algoritm [MCS]} and next try
     59Due to the huge 'exploding' possibilities it is not possible to walk trough the
     60whole list of possibilities, so we need alternative approaches to tackle this
     61problem. First we will try the \emph{Monte Carlo Search algorithm [MCS]} and next try
    6162\emph{Simulated Annealing [SA]}.
    6263
    6364\emph{MCS} is all about random sequence generation and trying to find a good
    64 solution, do a small ajustment on the solution and compare the new solution
     65solution, do a small adjustment on the solution and compare the new solution
    6566again.
    6667
    67 \emph{SA} takes a more and less the same aproch, but it also accept small
     68\emph{SA} takes a more and less the same approach, but it also accept small
    6869'losses' from time-to-time. But as time passes it get less likely to accept
    6970'bad solutions'.
     
    7172\section{Statistics}
    7273Of course many people have run numerous computer hours on finding the best
    73 possible fitness as shown in table~\ref{tab:best}. The algoritms used to find
     74possible fitness as shown in table~\ref{tab:best}. The algorithms used to find
    7475those numbers are not found.
    7576
     
    9293
    9394\section{Approach}
    94 The \emph{MCS} is implemented streight-forward from the wikipedia
     95The \emph{MCS} is implemented straight-forward from the Wikipedia
    9596page\footnote{http://en.wikipedia.org/wiki/Monte\_Carlo\_method}. The mutation
    9697choosing is flipping one bit in the array.
    9798
    98 For the \emph{SA} implemented also comes streight from 'the book'
     99For the \emph{SA} implemented also comes straight from 'the book'
    99100\footnote{http://en.wikipedia.org/wiki/Simulated\_annealing}, the process
    100 choosing for the cooling-down sequence is taken from the dutch wikipedia page
     101choosing for the cooling-down sequence is taken from the dutch Wikipedia page
    101102'Simulated annealing', which is nice indicator when to choose something when
    102 the solution is worse (logically, beter solutions will always be accepted).
     103the solution is worse (logically, better solutions will always be accepted).
    103104\footnote{http://nl.wikipedia.org/wiki/Simulated\_annealing}:
    104105\begin{math}
     
    119120
    120121\section{Results}
    121 All experiments are run 5 times the best solution is choosen and will be
     122All experiments are run 5 times the best solution is chosen and will be
    122123resented at table~\ref{tab:result}. Iteration size is set to 1000. For $n=20$
    123124the best fitness history is shown at figure~\ref{fig:fitness}.
     
    156157temperature function is not working as expected.
    157158
    158 Both algoritms are preforming much worse then the best found solutions.
     159Both algorithms are preforming much worse then the best found solutions.
    159160\section{Appendix 1}
    160161\label{app:code}
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