2024-02-16 00:05:14 -05:00
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<BODY BGCOLOR="#FFFFFF">
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<P><B><FONT SIZE="5">Chapter 7</FONT></B></P>
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<PRE> 7.1 <A HREF="#7.1">Tree Representation</A>
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7.2 <A HREF="#7.2">Selection Methods</A>
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7.3 <A HREF="#7.3">Operators</A>
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7.4 <A HREF="#7.4">Miscellany</A>
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7.4.1 <A HREF="#7.4.1">Tree Generation Spaces</A>
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7.4.2 <A HREF="#7.4.2">Saved Individuals</A>
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7.4.3 <A HREF="#7.4.3">Ephemeral Random Constants</A></PRE>
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<P>
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2024-02-13 21:21:51 -05:00
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2024-02-16 00:05:14 -05:00
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<HR>
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</P>
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<P>
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<B><FONT SIZE="5">Extending the Kernel<BR>
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</FONT></B>
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</P>
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<P>
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Internally, lil-gp is fairly simple. I have attempted to keep
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the structure reasonably clean and modular, without going too
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overboard about avoiding global variables and such. 24 C files
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comprise the kernel:<BR>
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</P>
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<P>
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<B>main.c</B> Initialization and cleanup.</P>
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<P>
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<B>gp.c</B> The main evaluate-and-breed cycle, and population
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statistics calculation.</P>
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<P>
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<B>eval.c</B> The tree evaluator.</P>
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<P>
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<B>tree.c </B>Utility routines dealing with trees--counting nodes
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and depth, printing, generating random trees, finding
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subtrees, copying trees.</P>
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<P>
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<B>change.c</B> Breeding of the new population each generation.
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</P>
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<P>
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<B>crossovr.c </B>The crossover operator.</P>
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<P>
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<B>reproduc.c </B>The reproduction operator.</P>
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<P>
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<B>mutate.c</B> The mutation operator.</P>
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<P>
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<B>select.c</B> Utility routines for selection methods. </P>
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<P>
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<B>tournmnt.c</B> The <B>tournament</B> selection method.</P>
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<P>
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<B>bstworst.c</B> The <B>best,</B> <B>worst</B>, and <B>random</B>
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selection methods.</P>
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<P>
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<B>fitness.c</B> The <B>fitness, fitness_overselect</B>, and <B>inverse_fitness</B>
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selection methods.</P>
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<P>
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<B>genspace.c</B> Utility routines for allocating space to grow
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new trees in.</P>
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<P>
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<B>exch.c</B> The subpopulation exchange system.</P>
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<P>
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<B>populate.c </B>Utility routines for population--copying, freeing,
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random generation.</P>
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<P>
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<B>ephem.c</B> Utility routines for ephemeral random constants.
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</P>
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<P>
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<B>ckpoint.c</B> Reading and writing checkpoint files. </P>
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<P>
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<B>event.c </B>System-dependent module for tracking execution
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time.</P>
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<P>
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<B>pretty.c</B> The tree pretty-printer.</P>
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<P>
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<B>individ.c</B> Utility routines for whole individuals--printing
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and calculating size.</P>
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<P>
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<B>params.c</B> The parameter database.</P>
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<P>
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<B>random.c</B> The portable pseudorandom number generator, adapted
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from <I>Numerical Recipes in FORTRAN</I>.</P>
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<P>
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<B>memory.c</B> The implementations of <B>MALLOC(), FREE(),</B>
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and <B>REALLOC()</B> that track memory usage.</P>
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<P>
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<B>output.c</B> The output subsystem (<B>oprintf()</B> and <B>oputs(),</B>
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among others).</P>
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<P> </P>
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<P>
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<B><A NAME="7.1">7.1 Tree Representation</A></B></P>
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<P>
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A tree is stored as an array of type <B>lnode.</B> An lnode is
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a union which can can a pointer to a function structure, a pointer
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to an ephemeral random constant (ERC) structure, or an integer.
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The tree is stored in prefix order. The first lnode is always
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a pointer to a function. If the function is an ERC terminal, then
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the next lnode in the array has the pointer to the ERC structure.
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If a function is of type EXPR (the user code controls evaluation
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of the function's arguments) then there is an extra lnode just
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before the start of each child_it contains an integer, the number
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of lnodes used in representing the child subtree. The evaluation
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code uses this value to skip the child during evaluation.</P>
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<P>
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Consider this expression in the symbolic regression problem:</P>
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<PRE>
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<B>(+ X (iflte X (* .34 X) .56))<BR></B></PRE>
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<P>
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This would be represented in lil-gp as the array:<FONT SIZE="2"></FONT></P>
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<P><FONT SIZE="2">
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<BR>
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</FONT>
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</P>
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<TABLE BORDER="0" ALIGN="DEFAULT" WIDTH="100%">
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">+ </TD>
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<TD ALIGN="LEFT" VALIGN="TOP">pointer to structure for function +
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">X </TD>
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<TD ALIGN="LEFT" VALIGN="TOP">pointer to structure for function X
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">iflte</TD>
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<TD>pointer to structure for function iflte
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">1</TD>
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<TD>
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2024-02-16 00:05:14 -05:00
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first argument to iflte takes 1 lnode to store
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">X</TD>
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<TD>
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">4 </TD>
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<TD>second argument to iflte takes 4 lnodes to store
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">* </TD>
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<TD>pointer to structure for function *
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">R</TD>
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<TD>pointer to structure for ERC function
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">.34</TD>
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<TD>pointer to structure containing ERC value
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">X</TD>
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<TD></TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">2</TD>
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<TD> third argument to iflte takes 2 lnodes to store
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</TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">R </TD>
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<TD></TD>
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</TR>
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<TR>
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<TD ALIGN="LEFT" VALIGN="TOP" WIDTH="10%">.56 </TD>
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<TD></TD>
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</TR>
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</TABLE>
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<P>
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<FONT SIZE="2"><BR>
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</FONT>
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</P>
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<P>
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This representation, while it may seem cumbersome, has two major
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advantages over a more traditional C representation (with individual
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structs for each node, linked by pointers). First, it uses much
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less memory--approximately 1-2 words per node versus the 4-5 it
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would take otherwise. This is because the structure of the tree
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is represented implicitly in the ordering of the nodes rather
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than explicitly via pointers. Second, it results in much faster
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tree evaluation. For instance, consider the crossover operator.
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In the traditional representation crossover is performed by just
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swapping two pointers. While this is very fast and easy, over
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time it means that the nodes of a given tree become spread out
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over the process's address space. On a system with virtual memory,
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this slows evaluation (or any traversal of the tree) to a crawl
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as the tree is spread across dozens of pages which must constantly
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be swapped in and out. lil-gp's representation complicates crossover
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somewhat, but leaves each offspring tree as a single continuous
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block of memory, able to fit on just one or two pages.<BR>
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</P>
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<P>
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<B><FONT SIZE="4"><A NAME="7.2">7.2 Selection Methods</A></FONT></B></P>
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<P>
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A selection method is implemented with two functions: one to perform
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initialization and cleanup, and another to do the actual selection.
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The first function, when called for initialization, creates and
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returns a data structure called a selection context. This contains
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any state information needed for the selection method. It should
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also store a pointer to the population that the selection is being
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done on. This structure will be passed to the second function,
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which should return an index of an individual within the population.
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</P>
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<P>
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To create a new selection method, it is suggested that you copy
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and modify an existing one (the <B>random</B> method in <B>bstworst.c</B>
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is an especially simple one). If your of selection can be expressed
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as randomly selecting an individual from a set where each individual
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has a fixed probability of selection, then there is already an
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efficient implementation of the second function. See the code
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for the <B>fitness, fitness_overselect,</B> and <B>inverse_fitness</B>
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methods for examples</P>
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<P>
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Finally, you must add a stt_record describing your selection method
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to the array <B>select_method_table</B> at the top of <B>select.c.</B>
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This lists the names and initialization functions of each selection
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method available.<BR>
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</P>
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<P>
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<B><FONT SIZE="4"><A NAME="7.3">7.3 Operator</A></FONT></B></P>
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<P>
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The breeding of the population is controlled by a table of breeding
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phases. This is built from the parameter database at the start
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of the run (and whenever <B>rebuild_breeding_table()</B> is called
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from user code. Each subpopulation has its own breeding table.
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For each phase, there is a stt_record in the table. Each stt_record has
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pointers to the four methods for the operator, the rate for that
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phase, and a pointer to an operator-specific structure</P>
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<P>
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To implement a new operator, it is suggested that you copy and
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modify the code of an existing operator. The reproduction operator
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is the simplest of the three included. Suppose that your new operator
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is called "foo". You would need to provide five functions
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(the following naming scheme is strongly recommended):<BR>
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</P>
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<P>
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<B>operator_foo_init</B> Parses the operator's options string
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and builds the operator table entry. The kernel
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functions <B>parse_o_rama()</B> and <B>free_o_rama()</B> are available
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to parse the options string just like the built-in operators do.
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<BR>
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</P>
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<P>
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<B>operator_foo_free</B> Frees the operator-specific part of the
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operator table entry.<BR>
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</P>
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<P>
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<B>operator_foo_start</B> This is called at the start of breeding.
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Selection methods should be initialized here.<BR>
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</P>
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<P>
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<B>operator_foo_end</B> This is called at the end of breeding.
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Selection contexts should be freed here.<BR>
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</P>
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<P>
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<B>operator_foo</B> This performs the actual operation.<FONT SIZE="2">
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<BR>
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</FONT>
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</P>
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<P>
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You then add a stt_record to the array <B>operator_table</B> at the
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top of <B>change.c</B>, listing the name of the operator (a string)
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and the operator's initialization method (a function pointer),
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for example, after adding the foo operator the table would look
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like:</P>
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<PRE>
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<B>operator operator_table[] =</B>
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<B>{ { "crossover", operator_crossover_init },</B>
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<B> { "reproduction", operator_reproduce_init },</B>
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<B> { "mutation", operator_mutate_init },</B>
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<B> { "foo", operator_foo_init },</B>
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<B> { NULL, NULL } };<BR>
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</B>
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</PRE>
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<P>
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The <B>next</B> field of the new population structure gives the
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index at which the operator should place the new individual. After
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adding an individual, increment the <B>next</B> field. If your
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operator produces multiple offspring with a single call then you
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must make sure that you don't overfill the population (the <B>next</B>
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field should not exceed the <B>size</B> field). The crossover
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operator, for instance, normally produces two offspring on each
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call. If it is called when there is only one more space in the
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population, it fills the space and throws the other offspring
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away.</P>
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<P>
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Your operator should always add at least one individual to the
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new population per call. If it does not, infinite loops may occur
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when the <B>probabilistic_operators</B> parameter is off.<BR>
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</P>
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<P>
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<B><FONT SIZE="4"><A NAME="7.4">7.4 Miscellany</A></FONT></B></P>
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<P>
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This section gives a general overview of how some of the nonobvious
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parts of lil-gp work and how they fit together.<BR>
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</P>
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<P>
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<B><A NAME="7.4.1">7.4.1 Tree Generation Spaces</A></B></P>
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<P>
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When building a new tree, lilgp needs a continuous block of memory
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to put the tree in, but can't allocate the final location of the
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tree because the size isn't known ahead of time. Therefore, special
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blocks of memory are allocated to grow the trees in. Every time
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an lnode is added to a tree-in- progress, the function <B>gensp_next</B>
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(or <B>gensp_next_int</B>) is called to enlarge the memory block
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if needed. Once the tree is finished and its size is known, its
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final location is allocated and the tree is copied from the generation
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space</P>
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<P>
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Currently there are two generation spaces needed. If you are implementing
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an operator or some- thing that requires three or more trees to
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be grown simultaneously, increase <B>GENSPACE_COUNT</B> in <B>defines.h.
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</B></P>
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<P>
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Generation spaces are initially allocated to hold <B>GENSPACE_START</B>
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lnodes, and grow in steps of <B>GENSPACE_GROW</B> lnodes. These
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constants are in <B>defines.h</B>.<BR>
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</P>
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<P>
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<B><A NAME="7.4.2">7.4.2 Saved Individuals</A></B></P>
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<P>
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lilgp tracks the best <I>n</I> individuals of each population,
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where <I>n</I> is a user-settable parameter. Pointers to these
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best individuals are passed to the user function <B>app_end_of_evaluation()</B>.
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To ensure that these pointers are always good, the kernel makes
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a copy of each individual in the top n and passes the address
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of the copy (since the original individual may not survive the
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breeding process)</P>
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<P>
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An individual can be referenced in multiple top-n lists (best-of-gen,
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best-of-run, etc.). To avoid making multiple copies, a reference
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count is kept for each individual. All these saved individuals
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are kept in a linked list, and once per generation a garbage collection
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procedure traverses the list and frees any individuals which have
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no references to them.<BR>
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</P>
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<P>
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<A NAME="7.4.3"><B>7.4.3 Ephemeral Random Constants</B></A><B></B></P>
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<P>
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Whenever a new ephemeral random constant terminal is inserted
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|
into a tree (during the generation of the initial population,
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|
or during mutation operations) the function <B>new_ephemeral_const()</B>
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|
is called to create the constant. It calls the user-supplied generation
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function to create a new value. Each ERC stt_record stores the value,
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along with a reference count of how many tree nodes point to that
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|
value. The ERC records are maintained in a linked list. Once per
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|
generation, a garbage collection routine traverses the linked
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list and removes any ERCs which are no longer needed (that have
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a reference count of zero)</P>
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<P>
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The ERC records are not allocated individually but in large blocks.
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This ensures that all the ERCs are kept on a few memory pages
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|
at most, which reduces the need for paging during evaluation (when
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|
there are many scattered references to the ERCs). When an ERC
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|
is freed by the garbage collection routine, it is added to the
|
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|
end of the free list. If the free list ever becomes empty, a new
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large block of ERC records is allocated and all of them added
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on to the end of the free list</P>
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<P>
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Pointers to the blocks themselves are kept in an array so that
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|
the blocks can be freed at the conclusion of the run. Since the
|
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|
number of blocks can increase, this array is reallocated as necessary.
|
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<BR>
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</P>
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2024-02-13 21:21:51 -05:00
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