428 lines
16 KiB
HTML
428 lines
16 KiB
HTML
<HTML>
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<HEAD>
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<TITLE>lilgpMonitor User's Manual</TITLE>
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</HEAD>
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<BODY bgcolor="#dfdfdf">
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<P>
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<h1><I>lilgpMonitor</I> User's Manual</h1>
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<P>
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<h2>1.1 Introduction</h2>
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<font size=3>
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<I>lilgpMonitor</I> is a companion utility for <I>lil-gp</I> that
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allows you to monitor the run-time progress of <I>lil-gp</I>
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experiments. It provides you with a mechanism to easily plot various
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genetic programming metrics such as mean standardized fitness, mean
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tree size, etc. After selecting a <I>lil-gp</I> statistics file, you
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may plot up to two curves on the same monitor using a variety of
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scaling techniques and line styles. You also have the ability to plot
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statistics for either the entire population or specific
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sub-populations. Several monitors can be used to simultaneously
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observe the trends of several genetic programming metrics. At the end
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of the experiment, the graphs can be saved in a GIF format, allowing
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them to be easily imported into word-processing documents.<BR>
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<P>
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If you are unfamiliar with genetic algorithms, genetic programming, or
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<I>lil-gp</I>, then you should begin by reading the <I>lil-gp User's
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Manual</I>.
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<P>
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</font>
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<h3>1.1.1 Features</h3>
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<font size=3>
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<I>lilgpMonitor</I> features
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include:
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<UL>
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<LI>Comparative Analysis: able to have several monitors open
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simultaneously, each plotting up to two GP metrics
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<LI>Useful: graphs can be exported as GIF files for easy import into
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word processors
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<LI>Timed updates: automatically re-reads statistics file and updates
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graphs at regular intervals
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<LI>Manual updates: can manually control graph updates
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<LI>Intelligent scaling function: automatically calculates 'nice' tick
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placements on the y-axis
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<LI>User specified scales: accepts maximum y-axis values from user
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<LI>Independent scale selection: plot metrics using independent scales
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<LI>Dependent scale selection: plot metrics using the same scale
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<LI>Sub-population support: Point and click selection of
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sub-population statistics
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<LI>Line styles: different line styles available
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<LI>Help: linked to on-line help system
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</UL>
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<P>
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</font>
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<h3>1.1.2 Limitations</h3>
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<font size=3>
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<I>lilgpMonitor</I> is capable of plotting all of the statistics
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generated by <I>lil-gp</I>, including the ability to plot statistics
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for specific sub-populations. In that regard, there are no
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limitations. However, there are limitations with respect to Java.
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Unfortunately, Java is slow, executing at about 1/20<SUP>th</SUP> the
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speed of compiled C code [Fla96]. This is especially evident when
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reading large statistics files. For example, loading a file
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containing statistics for a 50 generation experiment with 5
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sub-populations takes approximately 15 seconds on a Sparc20 with 64Mb
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RAM. There is hope; Sun claims that the performance of Java
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byte-codes converted to machine code is nearly as fast as native C or
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C++[Fla96].<BR>
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<P>
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The current version of <I>lilgpMonitor</I> does not perform any data
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manipulation such as smoothing. As a result, data sets containing
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localized spikes that are several orders of magnitude larger than the
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average trend cause curves to appear 'flat' when plotted. However,
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since the user has control over the maximum value for the y-axis,
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there is some mechanism for producing useful information in these
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cases. This may or may not be considered a limitation.
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<P>
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</font>
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<h3>1.1.3 Author</h3>
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<font size=3>
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This software was written by Ryan Shoemaker under the direction of
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Dr. William Punch. Other companion utilities for <I>lil-gp</I> have
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been developed by Ryan Shoemaker and Dave Guyette as part of a joint
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project. <BR>
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<P>
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Comments and suggestions may be sent to:
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<P>
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<blockquote>
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<FONT SIZE=3 FACE="Courier New">
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<a href="mailto:shoema16@cps.msu.edu">shoema16@cps.msu.edu</a><br>
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<a href="mailto:punch@cps.msu.edu">punch@cps.msu.edu</a><BR>
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<P>
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Department of Computer Science<br>
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3115 Engineering Building<br>
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Michigan State University<br>
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East Lansing, MI 48824<br>
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USA<br>
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</font>
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</blockquote>
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<P>
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</font>
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<h2>1.2 Getting Started </h2>
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<font size=3>
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When you unpack the distribution, you should get
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this directory tree:
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<P>
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<pre>
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<FONT SIZE=3 FACE="Courier New">
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lilgpMonitor/
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classes/ (byte-code files)
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docs/ (documentation files)
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images/ (image files used in documentation)
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javadocs/ (source-code documentation files)
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images/ (image files used in source-code documentation)
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src/ (source-code files)
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<BR>
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</FONT>
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</pre>
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<P>
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Before using <I>lilgpMonitor</I>, you must have Java JDK 1.02
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installed on your system. To generate the <FONT SIZE=3 FACE="Courier
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New">.class</FONT> files, change into the <FONT SIZE=3 FACE="Courier
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New">lilgpMonitor/src</FONT> directory and type:
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<P>
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<FONT SIZE=3 FACE="Courier New">javac -d ../classes *.java</FONT>
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<P>
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To run <I>lilgpMonitor</I>, simply change into the <FONT SIZE=3
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FACE="Courier New">lilgpMonitor/classes</FONT> directory and type:
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<P>
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<FONT SIZE=3 FACE="Courier New">java lilgpMonitor</FONT>
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<P>
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If you are running the utilities under Windows95/NT, you will have to
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modify the paths in the above commands and also add the <FONT SIZE=3
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FACE="Courier New">lilgpMonitor/classes</FONT> directory to your <FONT
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SIZE=3 FACE="Courier New">CLASSPATH</FONT> <I>in place of</I> the dot
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directory ".".<BR>
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<P>
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This is version 1.0 of <I>lilgpMonitor</I>. Updates and information
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related to <I>lilgpMonitor</I> can be found on the World Wide Web at
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<FONT SIZE=3 FACE="Courier New"><a href="http://isl.cps.msu.edu">
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http://isl.cps.msu.edu</a></FONT>.
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<P>
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</font>
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<h2>1.3 The Main Window</h2>
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<font size=3>
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The only window in <I>lilgpMonitor</I> is shown in Figure 1. This
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section will give a brief overview of the options available and the
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next section will give detailed explanations of each feature.<BR>
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<P>
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<CENTER>
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<img src="images/twoplot-ind.jpg"><p>
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<strong>Figure 1 - The Main Window</strong><p>
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</CENTER>
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<P>
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As you can see, there are four menus available in the menubar at the
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top of the window. From the <I>File</I> menu, you may open a
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statistics file, save your graph as a GIF file, create a new monitor,
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close the current monitor, or close all monitors. From the
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<I>Graph</I> menu, you may select a line style for the curves. The
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<I>Update</I> menu has options for selecting either manual updates or
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automatic timed updates. Finally, the <I>Help</I> menu provides
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access to the on-line help system and also information about the
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author and version of <I>lilgpMonitor</I> that you are using.<BR>
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<P>
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The actual graph sits in the middle of the window, with all of its
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controls along the bottom. In Figure 1, there are two curves on the
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graph, each with its own scale. The red scale appears on the left
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axis, while the blue scale appears on the right axis. The various
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controls on the bottom of the widow allow you to select a scale, the
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maximum scale values, and the GP metrics and sub-populations you want
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to plot.
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<P>
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</font>
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<h2>1.4 Feature Descriptions</h2>
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<font size=3>
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The features listed in this section apply only to the monitor in which
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you make the selection. For example, changing the automatic update
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interval to five minutes in one monitor will have no impact on the
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settings in any other monitors that are currently open or any monitors
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that are opened at a later time.
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<P>
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</font>
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<h3>1.4.1 The File Menu</h3>
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<font size=3>
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The <I>File</I> menu lets you perform file operations and
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create/destroy monitors.<BR>
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<UL>
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<LI>Selecting <I>Open</I> causes a file dialog to appear. Simply
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select the statistics file that you are interested in and it will be
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opened.
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<LI>Selecting <I>Save As</I> also causes a file dialog to appear.
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Simply supply a file name and the graph will be converted into GIF
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format and saved.
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<LI>Selecting <I>New Viewer</I> will create a new statistics monitor
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that contains all of the same features as the original monitor that
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you opened.
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<LI>Selecting <I>Close Viewer</I> will destroy the current monitor.
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Any other monitors that are currently open will remain open.
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<LI>Selecting <I>Exit All</I> will destroy all of the monitors that
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are currently open.
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</UL>
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<P>
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</font>
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<h3>1.4.2 The Graph Menu</h3>
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<font size=3>
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The <I>Graph</I> menu lets you select the line style
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you would like to apply to the curves.
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<UL>
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<LI>Selecting <I>Lines</I> will cause the curves to be plotted using
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line segments to connect the data points, as shown in Figure 2.
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<LI>Selecting <I>Lines + Points</I> will cause the curves to plotted
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using line segments to connect the data points, but will also draw a
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small diamond indicating the actual positions of the data points on
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the curve, as shown in Figure 3.
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</UL>
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<P>
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<CENTER>
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<img src="images/lines.gif"><p>
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<strong>Figure 2 - Lines</strong><p>
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</CENTER>
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<P>
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<CENTER>
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<img src="images/linespoints.gif"><p>
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<strong>Figure 3 - Lines + Points</strong><p>
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</CENTER>
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<P>
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</font>
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<h3>1.4.3 The Update Menu</h3>
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<font size=3>
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The <I>Update</I> menu lets you select the timing of graph updates.
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<UL>
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<LI>Selecting <I>Manual Updates/Update Now</I> does two things.
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First, is stops the automatic update timer if it is currently set.
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Second, it reads the contents of the statistics file and refreshes the
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graph immediately.
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<LI>Selecting any of the update intervals sets the timer to update the
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graph automatically. For example, selecting <I>5 Minutes</I> will
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cause the monitor to re-read the statistics file and refresh the graph
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every five minutes.
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</UL>
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<P>
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</font>
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<h3>1.4.4 The Help Menu</h3>
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<font size=3>
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<UL>
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<LI>Selecting <I>Help</I> provides access to the on-line help system.
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<LI>Selecting <I>About</I> provides information about the version of
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lilgpMonitor that you are using.
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</UL>
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<P>
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</font>
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<h3>1.4.5 Selecting a Scale</h3>
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<font size=3>
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Below the graph, there are three boxes labeled '<I>Red Scale</I>',
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'<I>Independent Scales</I>', and '<I>Blue Scale</I>.' These controls
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allow you to choose which scales to use and specify maximum scale
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values. <BR>
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<P>
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The default setting for the scale is <I>Independent</I>
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A<I>utomatic</I> scaling. This means that <I>lilgpMonitor</I> will
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examine the data set and determine the best scales to use when
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plotting the curves. Figure 4 shows the result of using this
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combination of settings. In this case, the red curve is plotted using
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the scale on the left axis, while the blue curve is plotted using the
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scale on the right axis. These curves are plotted using
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<I>independent</I> scales. Since there are no values supplied in the
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<I>y-max</I> fields, <I>lilgpMonitor</I> automatically selects values
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that will allow the entire data set to be visible within the graph -
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this is <I>automatic</I> scaling. These settings are useful when you
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would like to view two GP metrics that are unrelated
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(i.e. standardized fitness versus tree size).
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<P>
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<CENTER>
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<img src="images/twoplot-ind.jpg"><p>
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<strong>Figure 4 - Independent Autoscaling</strong><P>
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</CENTER>
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<P>
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By clicking on either the '<I>red</I>' or '<I>blue</I>' checkboxes,
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you will be selecting a <I>dependent</I> scaling mechanism with
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<I>automatic</I> scaling. This means that <I>lilgpMonitor</I> will
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apply the selected scale to both curves. Figure 5 shows the red scale
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being applied to both curves, while Figure 6 shows the blue scale
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being applied to both curves. These settings are useful when you
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would like to compare two GP metrics that are related
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(i.e. standardized fitness of sub-population 1 versus standardized
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fitness of sub-population 2). Looking back at Figure 4, you can see
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how it can be difficult to determine the relationship between two GP
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metrics when using <I>Independent Autoscaling</I>. It isn't until you
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examine Figure 5 where <I>Dependent Autoscaling</I> is used that you
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can clearly see the relationship between the two curves. Since the
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blue curve falls below the red curve in every generation, the red
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curve is not visible when <I>Dependent Autoscaling</I> is set on the
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blue curve in Figure 6.
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<P>
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<CENTER>
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<img src="images/twoplot-red.jpg"><p>
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<strong>Figure 5 - Red Dependent Autoscaling</strong><p>
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</CENTER>
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<P>
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<CENTER>
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<img src="images/twoplot-blue.jpg"><p>
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<strong>Figure 6 - Blue Dependent Autoscaling</strong><p>
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</CENTER>
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<P>
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By changing the values in the <I>y-max</I> fields, you are selecting
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<I>manual</I> scaling. It is possible to have <I>manual</I> scaling
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active for one curve and <I>automatic</I> scaling active for the other
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curve by supplying a value for <I>y-max</I> in only one of the
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textfields. It is important to understand that when you select
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<I>manual</I> scaling, you are not actually specifying the scale of
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the curve, <I>per se</I>, but rather the maximum value that will be
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used to in determining the scale. For example, specifying a
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<I>y-max</I> value of 0.5 for the red curve will cause it to truncate
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the top of the tallest spikes as shown in Figure 7.
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<P>
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<CENTER>
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<img src="images/twoplottrunc.jpg"><p>
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<strong>Figure 7 - Independent Manual Scaling</strong><p>
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</CENTER>
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<P>
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</font>
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<h3>1.4.6 Selecting the GP Metrics</h3>
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<font size=3>
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Below the scale selection boxes, there are two pull-down list menus
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that allow you to choose the GP metrics to display. The default
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setting is <I>Gen Fitness - Mean</I> for the red curve and <I>None</I>
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for the blue curve. Figure 8 outlines all of the available metrics
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and their abbreviations. <BR>
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<BR>
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<center>
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<TABLE BORDER=1>
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<TR bgcolor="#aaaaff"><TD><CENTER>GP Metric</CENTER>
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</TD><TD><CENTER>Abbreviation Used in <I>lilgpMonitor</I></CENTER>
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</TD></TR>
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<TR><TD>mean standardized fitness of generation</TD>
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<TD>Gen Fitness - Mean</TD></TR>
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<TR><TD>standardized fitness of best-of-generation individual
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</TD><TD>Gen Fitness - Best</TD></TR>
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<TR><TD>standardized fitness of worst-of-generation individual
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</TD><TD>Gen Fitness - Worst</TD></TR>
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<TR><TD>mean tree size of generation</TD><TD>Gen Tree Size - Mean
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</TD></TR>
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<TR><TD>mean tree depth of generation</TD><TD>Gen Tree Depth - Mean
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</TD></TR>
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<TR><TD>tree size of best-of-generation individual</TD>
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<TD>Gen Tree Size - Best</TD></TR>
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<TR><TD>tree depth of best-of-generation individual
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</TD><TD>Gen Tree Depth - Best</TD></TR>
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<TR><TD>tree size of worst-of-generation individual
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</TD><TD>Gen Tree Size - Worst</TD></TR>
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<TR><TD>tree depth of worst-of-generation individual
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</TD><TD>Gen Tree Depth - Worst</TD></TR>
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<TR><TD>mean standardized fitness of run</TD><TD>Fitness - Mean
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</TD></TR>
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<TR><TD>standardized fitness of best-of-run individual
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</TD><TD>Fitness - Best</TD></TR>
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<TR><TD>standardized fitness of worst-of-run individual
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</TD><TD>Fitness - Worst</TD></TR>
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<TR><TD>mean tree size of run</TD><TD>Tree Size - Mean
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</TD></TR>
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<TR><TD>mean tree depth of run</TD><TD>Tree Depth - Mean
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</TD></TR>
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<TR><TD>tree size of best-of-run individual</TD><TD>Tree Size - Best
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</TD></TR>
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<TR><TD>tree depth of best-of-run individual</TD><TD>Tree Depth - Best
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</TD></TR>
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<TR><TD>tree size of worst-of-run individual</TD><TD>Tree Size - Worst
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</TD></TR>
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<TR><TD>tree depth of worst-of-run individual</TD><TD>Tree Depth - Worst
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</TD></TR>
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</TABLE>
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<p>
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<strong>Figure 8 - Available GP Metrics</strong>
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</CENTER>
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</font>
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<P>
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<h3>1.4.7 Selecting the Sub-Populations</h3>
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<font size=3>
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Below the GP metric selection components, there are two list boxes
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that allow you to select which sub-population you would like to
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monitor. The default setting is to view the overall statistics.
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Using these list boxes is more clumsy than it should be due to
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bugs in Java's AWT event model. Unfortunately, on the x86 port
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of Java, list selection events only occur if you double-click
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on the list item. Even though selecting a list item with a single-click
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<I>appears</I> to change the list selection, it does not generate
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a list selection event, at least not on x86 machines. Therefore,
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in an effort to remain consistent on every platform, you must
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double-click the list items to select the sup-population. The
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currently selected sub-population is preceded by an asterisk -
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the sub-population that is obviously highlighted may not be the
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sub-population that is actually being displayed in the graph.
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Be careful!<BR>
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</font>
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</BODY>
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</HTML>
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