gaa.html

GaPlayground:一个非常好的遗传算法demo.

<A NAME="Feedback"></A><U><H4>Limitations and Request for feedback</H4></U>
The GA Playground is currently at a preliminary stage, still under construction. The options for definitions of new problems are not fully implemented yet, and there is no Java documentation, so it is currently limited to playing with the defined problems. When playing with the parameters (or preparing new parameters files in the application mode), please be careful with your input parameters, as there is no protection against illegal entries<p> 

I shall be glad to receive any comments or suggestions. Please use my <A HREF="mailto:[e-mail]">mailbox </A>for any sort of feedback. 


<p>
<A NAME="Examples"></A><center><U><H3>Examples and Test Problems</H3></U></center>

<A NAME="Examples General"></A><U><H4>General</H4></U>

<p>

If you will look at the source of any of the following Demo Html pages (by the View Source function in your browser), you will see that each activates the same Java class file (GaaApplet.class), the only difference is the ascii definition file that is given as a parameter to the applet. In each case, all the class files used are the same. The only exception is the GaaFunction.class, which either should be specific for each problem definition, or contain several alternative functions that are activated according to the specific problem code.<p>

Once you load a specific example, you can change the problem definition (by modifying problem attributes, variables ranges and variables mapped values) and also experiment with the genetic algorithm by playing with any of the GA parameters.

<p>

<A NAME="Examples Notes"></A><U><H4>Important Notes:</H4></U>
<UL>
<LI>The applet requires a browser that supports <B>JDK 1.1.5</B> or above (Communicator 4.05 (Preview Release), MSIE 4.01, HotHava 1.1).
<LI>If you browser does not support JDK 1.1.5 you can download the free Sun's Java Plugin: http://www.javasoft.com/products/plugin/ (good for Netscape 3.0 and above, MSIE 3.02 and above).
<LI>The applet has a relatively long loading time, especially when loaded for the first time. 
<LI>Once the applet is loaded (with any of the demo problems), it is possible to load any other problem from within the applet, through the 'GA/Entry Screen' menu command.
<LI>The applet is best viewed in 800x600 (or higher) screen resolution
</UL>
<br>
<p>

<A NAME="Demo Problems"></A><center><U><H3>List of Demo Problems</H3></U></center>

<center>
<TABLE Border=0 CELLPADDING=5 CELLSPACING=5>

<TR>
<TD><A NAME="Multiple Problems"></A><B>Multiple  Problems Applets</B></TD>
<TD>    </TD>
</TR>

<TR>
<TD><A NAME="Demo All"></A><A HREF="GaaAll.html">All Demos</A></TD>
<TD>All the demo problems: In this configuration you can select any of the examples listed below, and switch between them. Selection is done from the applet's 'Entry Screen' (GA menu)</TD>
</TR>

<TR>
<TD><A NAME="Demo Tsp"></A><B>TSP:</B></TD>
<TD> </TD>
</TR>

<TR>
<TD><A NAME="TspDemo"></A><A HREF="TspDemo.html">TSP on circle</A></TD>
<TD>A Tsp where all cities are located on a circle. The number of cities is user definable.</TD>
</TR>

<TR>
<TD><A NAME="TspBayg29"></A><A HREF="TspBayg29.html">TSP Bayg29</A></TD>
<TD>TSP of 29 Cities in Bavaria (Groetschel,Juenger,Reinelt)</TD>
</TR>

<TR>
<TD><A NAME="TspAtt48"></A><A HREF="TspAtt48.html">TSP Att48</A></TD>
<TD>TSP of 48 capitals of the US (Padberg/Rinaldi)</TD>
</TR>

<TR>
<TD><A NAME="Demo Knapsack"></A><B>Knapsack:</B></TD>
<TD> </TD>
</TR>

<TR>
<TD><A NAME="Knapsack01"></A><A HREF="Knapsack01.html">Single<br>0/1-Knapsack</A></TD>
<TD>A single knapsack problem with 50 objects</TD>
</TR>

<TR>
<TD><A NAME="KnapsackWeing1"></A><A HREF="KnapsackWeing1.html">Weing1: multiple 0/1-Knapsack</A></TD>
<TD>A multiple knapsack problem with 2 knapsacks and 28 objects</TD>
</TR>

<TR>
<TD><A NAME="KnapsaclWeish01"></A><A HREF="KnapsackWeish01.html">Weish01: multiple 0/1-Knapsack</A></TD>
<TD>A multiple knapsack problem with 5 knapsacks and 30 objects</TD>
</TR>

<TR>
<TD><A NAME="Demo BP"></A><B>Bin Packing:</B></TD>
<TD> </TD>
</TR>

<TR>
<TD><A NAME="Binpack1_00"></A><A HREF="Binpack1_00.html">Binpack1 u120_00</A></TD>
<TD>120 objects uniformly distributed in (20,100), bins of size 150</TD>
</TR>

<TR>
<TD><A NAME="Binpack5_19"></A><A HREF="Binpack5_19.html">Binpack5 t60_19</A></TD>
<TD>60 objects in 'triplets' of items from (25,50), bins of size 100</TD>
</TR>

<TR>
<TD><A NAME="Demo FA"></A><B>Facility Allocation:</B></TD>
<TD> </TD>
</TR>

<TR>
<TD><A NAME="Steiner"></A><A HREF="Steiner.html">Steiner on circle</A></TD>
<TD>A facility allocating problem (where all cities are on a circle)</TD>
</TR>

<TR>
<TD><A NAME="SteinerByFile"></A><A HREF="SteinerByFile.html">Steiner by file</A></TD>
<TD>A facility allocating problem (where city coordinates are read from a file)</TD>
</TR>

<TR>
<TD><A NAME="Demo Multi-modal"></A><B>Multi-modal Functions:</B></TD>
<TD> </TD>
</TR>

<TR>
<TD><A NAME="Ackley"></A><A HREF="Ackley.html">Ackley's Function</A></TD>
<TD>A multi-modal test function:<br>
Minimize f(x) = 20+e-20*exp(-0.2*exp(sqrt((1/n)*sum(x(i)^2))-exp((1/n)*sum(cos(2*Pi*x(i)))
</TD>
</TR>

<TR>
<TD><A NAME="Rosenbrock"></A><A HREF="Rosenbrock.html">Rosenbrock's Function</A></TD>
<TD>A multi-modal test function:<br>
Minimize f(x) = sum(100*(x(i)-x(i-1)^2)^2 + (1-x(i-1))^2
</TD>
</TR>

<TR>
<TD><A NAME="Schwefel"></A><A HREF="Schwefel.html">Schwefel's Function</A></TD>
<TD>A multi-modal test function:<br>
Minimize f(x) = 418.9829*n + sum(-x(i)*sin(sqrt(abs(x(i))))
</TD>
</TR>

<TR>
<TD><A NAME="Rastrigin"></A><A HREF="Rastrigin.html">Rastrigin's Function</A></TD>
<TD>A multi-modal test function:<br>
Minimize f(x) = 10.0*n + sum(x(i)^2 - 10.0*cos(2*Pi*x(i)))
</TD>
</TR>

<TR>
<TD><A NAME="Griewank"></A><A HREF="Griewank.html">Griewank's Function</A></TD>
<TD>A multi-modal test function:<br>
Minimize f(x) = 1/4000*sum(x(i)-100)^2 - prod((x(i)-100)/sqrt(i)) + 1
</TD>
</TR>


<TR>
<TD><A NAME="Demo Functions"></A><B>Function Optimization:</B></TD>
<TD> </TD>
</TR>

<TR>
<TD><A NAME="SphereModel"></A><A HREF="SphereModel.html">Sphere Model</A></TD>
<TD>A well known test function:<br>
Minimize f(x) = sum((x(i)-1)^2)</TD>
</TR>

<TR>
<TD><A NAME="SingleVarMin"></A><A HREF="SingleVarMin.html">Single Variable Minimization</A></TD>
<TD>Minimize f(x) = x^4 - 12*x^3 + 15*x^2 + 56*x - 60
</TD>
</TR>

<TR>
<TD><A NAME="MultiVarMin"></A><A HREF="MultiVarMin.html">Multi Variable Minimization</A></TD>
<TD>Minimize f(x1,x2,x3,x4,x5) = x1*sin(x1) + 1.7*x2*sin(x1) - 1.5*x3 - 0.1*x4*cos(x4+x5-x1) + (0.2*x5^2-x2) - 1
</TD>
</TR>

<TR>
<TD><A NAME="Simpleton"></A><A HREF="Simpleton.html">Simpleton</A></TD>
<TD>A Trivial 10 Variables Maximization Problem:<br>Maximize: (x1*x2*x3*x4*x5)/(x6*x7*x8*x9*x10) where (x1..x10)=[1..10]</TD>
</TR>

</TABLE>
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