184 lines
6.2 KiB
HTML
184 lines
6.2 KiB
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<BODY BGCOLOR="#FFFFFF">
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<P><B><FONT SIZE="5">Chapter 1
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</FONT></B></P>
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<PRE>
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1.1 <A HREF="#1.1">Features
</A>
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1.2 <A HREF="#1.2">Kernel</A>
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1.3 <A HREF="#1.3">Specification of Problems
</A>
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1.4 <A HREF="#1.4">Multiple Populations
</A>
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1.5 <A HREF="#1.5">Availability
</A>
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1.6 <A HREF="#1.6">Author</A></PRE>
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<P>
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<HR>
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</P>
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<P>
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<B><FONT SIZE="5">Introduction</FONT></B></P>
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<P>
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lil-gp is a C language system for developing genetic
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programming applications based on the LISP
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work of John Koza at Stanford University.
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lil-gp evolves trees whose nodes are C function pointers,
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so tree evaluation is done entirely with complied
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code. This gives us a manyfold speed increase,
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allows us to handle much large problems (bigger
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populations, more generations), and is portable to a wide
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variety of platforms. lil-gp individuals can be composed
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of multiple trees, some of which can evaluate
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the others to give ADF capabilities. This
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system is the first (that we know of) to support multiple-population
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runs with arbitrary interchange topologies. lil-gp
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has many many options for controlling breeding, and is capable
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of emulating the "Simple LISP Code," given
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in the appendix of Koza's book [3 ], as well
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as the parameters given in Chapter 7, "Detailed Description
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of Genetic Programming," of that book.<FONT><BR>
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</FONT>
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</P>
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<P>
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<B><FONT SIZE="4"><A NAME="1.1">1.1 Features</A></FONT></B></P>
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<P>
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lil-gp features include:</P>
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<UL>
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<LI>ease-of-use: options are specified via a flexible format parameter
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file; command line arguments lend the system to batch operation
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for long series of runs
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</LI>
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</UL>
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<UL>
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<LI>supports multiple subpopulations with arbitrary exchange topologies
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<BR>
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</LI>
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<LI>written in C for portability to a wide variety of Unix/DOS/Mac
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platforms<BR>
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</LI>
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<LI>7 selection methods: fitness proportionate, greedy overselection,
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inverse fitness, variable size tournament, random, best, worst
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<BR>
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</LI>
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<LI>3 genetic operators: crossover, reproduction, mutation<BR>
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</LI>
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<LI>writes and restarts from checkpoint files <BR>
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</LI>
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<LI>optional node and/or depth limitations on tree size<BR>
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</LI>
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<LI>support for ephemeral random constants<BR>
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</LI>
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<LI>extensive output, including statistics files ready for import
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into plotting/analysis packages<BR>
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</LI>
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</UL>
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<P>
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<B><FONT SIZE="4"><A NAME="1.2">1.2 Kernel</A></FONT></B></P>
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<P>
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At lil-gp's core is a tree representation that is compact yet
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amenable to extremely fast evaluation. Trees are stored as preorder
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expressions, with special symbols inserted to indicate ephemeral
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random constants and conditionally evaluated subtrees. The compactness
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of the representation allows larger populations to be stored in
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real memory, and the fact that trees are stored as contiguous
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blocks of memory minimizes the impact of paging on performance.
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The selection methods and genetic operators are implemented in
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an object-oriented fashion, allowing new routines to be added
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easily. Checkpoint files save the entire state of the run, allowing
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a promising run to be extended, or an interrupted run to be completed.
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A portable random number generator provides consistent results
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across platforms.<BR>
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</P>
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<P>
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<B><FONT SIZE="4"><A NAME="1.3">1.3 Specification of Problems</A></FONT></B></P>
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<P>
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Creating a program in lil-gp is done with a minimum of code writing.
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The use must provide one C function for each GP function and terminal,
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in addition to a fitness evaluation function. Several callback
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hooks are provided for initialization, customized output, and
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reading and writing user state information to checkpoint files.
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Once the user code is written, the executable need only be compiled
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once_all parameters and operators are available_even switch between
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single and multiple population runs_just by modifying the parameter
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file.<BR>
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</P>
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<P>
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<B><FONT SIZE="4"><A NAME="1.4">1.4 Multiple Populations</A></FONT></B></P>
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<P>
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lil-gp's support for multiple population problems includes user-specified
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exchange topology and exchange interval. The parameter file specifies
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how the individuals to be exchanged from each subpopulation are
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selected, and where they go. The output files include statistics
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on fitness and tree size for each subpopulation and for the entire
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population, both for the current generation and the entire run
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to that point.<BR>
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</P>
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<P>
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<B><FONT SIZE="4"><A NAME="1.5">1.5 Availability</A></FONT></B></P>
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<P>
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This is version 1.0 of lil-gp. The distribution includes implementations
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of three of the problems from Koza's first book [3]--the Boolean
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11-multiplexer, symbolic regression, and the artificial ant. Two
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problems from the second book [4] using ADFs are also implemented:
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the two-boxes problem and the lawnmower problem. This documentation
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covers building and running the sample problems, writing your
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own applications, and extending the kernel.<BR>
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</P>
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<P>
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<B><FONT SIZE="4"><A NAME="1.6">1.6 Author</A></FONT></B></P>
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<P>
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lil-gp was written by Douglas Zongker, under the direction of
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Dr. Bill Punch. Mike Boers, Mike Raymer, Dr. Erik Goodman, and
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the rest of the MSU GARAGe (Genetic Algorithms Research and Applications
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Group) also provided valuable assistance.
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</P>
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<P>
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Other users of lil-gp have provided suggestions and solutions
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for compatibility problems. Thanks especially go to Nigel Dodd,
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Kayvan Sylvan, Pierce T. Wetter III, and Glen Ropella.<BR>
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</P>
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<P>
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We want to hear about your experiences with lil-gp. We welcome
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your questions, comments, sugges- tions, and, of course, bug reports.1
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lil-gp related e-mail can be sent to zongker@isl.cps.msu.edu.
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You should always be able to find any new information on lil-gp
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on the World Wide Web, at the URL http://isl.cps.msu.edu/GA/software/lil-gp.
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Paper mail may be sent to:<BR>
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</P>
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<BLOCKQUOTE>
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Computer Science Department
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</BLOCKQUOTE>
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<BLOCKQUOTE>
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A-714 Wells Hall
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</BLOCKQUOTE>
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<BLOCKQUOTE>
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Michigan State University
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</BLOCKQUOTE>
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<BLOCKQUOTE>
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East Lansing, MI 48824
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</BLOCKQUOTE>
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<BLOCKQUOTE>
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USA<BR>
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</BLOCKQUOTE>
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<P>
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<FONT SIZE="2">1 Well, we don't actually welcome bug reports, but
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send them anyway.</FONT></P>
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</BODY>
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</HTML>
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