PH.D. DISSERTATION AVAILABLE:

                     CENG Technical Report 94-13

		  Recombination, Selection, and the
	      Genetic Construction of Computer Programs

			 Walter Alden Tackett

		    Computer Engineering Division
	    Department of Electrical Engineering - Systems
		  University of Southern California
               Los Angeles, California, USA 90089-2562

Bound copies are available from the University for $25 apiece, and
will be available from University Microfilms as well.  An anonymous
ftp version is available from santafe.edu in the directory
pub/Users/tackett/phd, and will also be available from the Genetic
Programming archives at ftp.cc.utexas.edu.  Other sites TBD.  File
information and abstract are given below.

The directory contains five files (in theory):

README (this file)

watphd_1.ps.Z 
watphd_2.ps.Z 
watphd_3.ps.Z 
	- The dissertation is broken up into three equal-sized parts in 
	  compressed postscript format, suitable for unix, Mac, etc. 
	  The postscript was generated with a MSWindows driver for
	  Adobe Postscript v3.0 cartridge on an HP LaserJet IIP.  Filtered
	  via dos2unix -ascii and compressed with the Unix compress command.

wtphd_mw.zip
	- Same three parts, in original form as .doc files generated by 
	  MSWord 6.0a under Windows/NT, and compressed/archived via 
	  pkzip -ex.  Use this if you have Word 6 (sorry to hear that), not
	  compatible with earlier versions of Word or with Word on the Mac.

Abstract:

		  Recombination, Selection, and the
	      Genetic Construction of Computer Programs

			 Walter Alden Tackett
		  University of Southern California

			       Abstract

Computational intelligence seeks as a basic goal to create artificial
systems which mimic aspects of biological adaptation, behavior,
perception, and reasoning.  Toward that goal, genetic program
induction - "Genetic Programming" - has succeeded in automating an
activity traditionally considered to be the realm of creative human
endeavor.  It has been applied successfully to the creation of
computer programs which solve a diverse set of model problems.  This
naturally leads to questions such as:

* Why does it work?  

* How does it fundamentally differ from existing methods?

* What can it do that existing methods cannot?

The research described here seeks to answer those questions through
investigations on several fronts.  Analysis is performed which shows
that Genetic Programming has a great deal in common with heuristic
search, long studied in the field of Artificial Intelligence.  It
introduces a novel aspect to that method in the form of the
recombination operator which generates successors by combining parts
of favorable strategies.  On another track, we show that Genetic
Programming is a powerful tool which is suitable for real-world
problems.  This done first by applying it to an extremely difficult
induction problem and measuring performance against other
state-of-the-art methods.  We continue by formulating a model
induction problem which not only captures the pathologies of the real
world, but also parameterizes them so that variation in performance
can be measured as a function of confounding factors.  At the same
time, we study how the properties of search can be varied through the
effects of the selection operator.  Combining the lessons of the
search analysis with known properties of biological systems leads to
the formulation of a new recombination operator which is shown to
improve induction performance.  In support of the analysis of
selection and recombination, we define problems in which structure is
precisely controlled.  These allow fine discrimination of search
performance which help to validate analytic predictions.  Finally, we
address a truly unique aspect of Genetic Programming, namely the
exploitation of symbolic procedural knowledge in order to provide
"explanations" from genetic programs.