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<h2>Optimization</h2>
The Optimization panel allows to test, tweak and observe how
different algorithms perform a function optimization in a
2-dimensional parameter space: "the canvas". The value (or reward)
of the function at a specific position in parameter space is
displayed by the amount of red, which can be painted on using the
Paint Reward tool in the drawing options.<br>
<br>
The canvas will display the process of optimization from a given
starting position (provided by the "Drag me" drag-and-drop button.
If no starting position is provided, a random position in parameter
space will be selected. <br>
<br>
The canvas displays different information in multiple layers, which
can be toggled using the display options. These are:
<ul>
<li>Samples: the original function value (reward), high colors
correspond to high reward value</li>
<li>Learned Model: the optimization history (lined path), current
best parameters (green circles), visited parameter instances
(black circles), and (optionally) additional model information
(e.g. set of active particles)</li>
<li>Model Info: representation of the reward function as elevation
contour lines</li>
<li>Legend: the current maximum value found (the maximum is always
normalized to 1) </li>
</ul>
A yellow zone indicates the region of parameter space in which the
function value is maximum or higher than a given Stop Criterion. The
optimization process will stop after a set number of iterations has
been performed, or when a sufficient function value is reached.<br>
<br>
<span style="font-weight: bold;">In Practice</span><br>
The easiest way to test optimization is to:
<ol>
<li>Paint some reward (left-click) in the canvas </li>
<li>Click on "Optimize"</li>
</ol>
This should initialize the algorithm and start animating the
exploration of the parameter space.<br>
<br>
<span style="font-weight: bold;">Options and Commands</span><br>
The interface for optimization (the right-hand side of the Algorithm
Options dialog) provides the following commands:<br>
<ul>
<li>Optimize: Initialize and start the optimization using the
currently selected algorithm and options</li>
<li>Stop/Start: pause or restart the optimization process (will
not reset the iteration count)<br>
</li>
<li>Clear: clear the current regression model (does NOT clear the
data)</li>
</ul>
and the following options:
<ul>
<li>Starting Position: (draggable) defines the starting position
for the optimization process. Re-drag to remove</li>
<li>Max Iterations: Maximum number of iterations to compute </li>
<li>Stop Criterion: Target minimal value to be attained before
stopping (range: [0, 1]). </li>
</ul>
All other options are algorithm-dependent and should be described in
the help menu of the algorithm itself.<br>
<br>
<span style="font-weight: bold;">Generate Rewards</span><br
style="font-weight: bold;">
It is possible to generate a set of pre-constructed rewards by
dragging and dropping a gaussian of fixed size (Var option) or a
gradient from the center of the canvas to the dropped position.
Alternatively a number of standard benchmark functions is proposed.
Use the Set button to draw the benchmark function onto the canvas.<br>
<br>
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