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{\bf Can Downhill Walkers from the Top of a Hill Climb it Again?} 

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{\bf
Akira Imada\\
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Dokuz Eylul University\\
Izmir, Turkey
}
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{\bf Random Hillclimbers}\\
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$\Downarrow$\\
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{\bf that Walk Down a Hill}\\
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$\Downarrow$\\
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{\bf to Reveal the Shape of the Hill.}
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{\bf We evolve neural networks}\\
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{\bf searching for\\ 
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appropriate weight configurations}\\
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{\bf where solutions are not usually unique.}\\
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{\bf We need an information of Weight Space.}
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$\Box${\bf Contents:}\\

\begin{itemize}
\item What is Associative Memory?
\item How we evolve it?\\
\item[] \underline{\hspace*{5in}} \\
\item Exploration on High-D Fitness Landscape\\
   \begin{itemize}
   \item[$\cdot$]using a {\it Hill-climbing technique.}\\
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\item[]\underline{\hspace*{5in}} \\
\item Concluding Remarks
\item Future Work
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{\bf For an evolution to start} \\
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we need \\
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{\it fitness} of each individual to the environment.\\
(how good is an individual?)\\
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In our example:\\
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 {\it the degree to how a weight configuration works 
     as an associative memory}

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\underline{\hspace*{7in}} \\
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At the start: \\
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$\star$ fitness values are low; \\
but \\
$\star$ different from each other!
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{\bf We have many peaks found by EC's.\\
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But EC's have never located the Hebbian peak.\\
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Why does the Hebbian Peak Resist to be Found?}
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$\Box$ {\bf Can Walkers Climb the Hill Again?}
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{\bf $\Box$ Conclusions:}\\
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\item We have many different weight solutions for Associative Memory,
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   \item How do they look in the fitness landscape? 
   \item Clasical but there still remains many unknown issues.
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\item[]
\item EP downhill walkers reveal the shape of the hills;
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\item We shed new light on the classical unknown issues.
\item Can we apply these techniques to real world problems?
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