Algebraic simplification of GP programs during evolution

Program bloat is a fundamental problem in the field of Genetic Programming (GP). Exponential growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Simplification is an attempt to remove such redundancies from programs. This paper looks at the effects of applying an algebraic simplification algorithm to programs during the GP evolution. The GP system with the simplification is examined and compared to a standard GP system on four regression and classification problems of varying difficulty. The results suggest that the GP system employing a simplification component can achieve superior efficiency and effectiveness to the standard system on these problems.

[1]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Wolfgang Banzhaf,et al.  Automatic Generation of Control Programs for Walking Robots Using Genetic Programming , 2002, EuroGP.

[3]  Lothar Thiele,et al.  Genetic Programming and Redundancy , 1994 .

[4]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[5]  Mengjie Zhang,et al.  Multiclass Object Classification Using Genetic Programming , 2004, EvoWorkshops.

[6]  John R. Koza,et al.  Genetic Programming IV: Routine Human-Competitive Machine Intelligence , 2003 .

[7]  Victor Ciesielski,et al.  Genetic Programming for Multiple Class Object Detection , 1999, Australian Joint Conference on Artificial Intelligence.

[8]  Gaston H. Gonnet,et al.  Determining equivalence of expressions in random polynomial time , 1984, STOC '84.

[9]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[10]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[11]  Vic Ciesielski,et al.  Representing classification problems in genetic programming , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[12]  Harald Niederreiter,et al.  Introduction to finite fields and their applications: Preface , 1994 .

[13]  Wade Trappe,et al.  Introduction to Cryptography with Coding Theory , 2002 .

[14]  Rune B. Lyngsø,et al.  Lecture Notes I , 2008 .

[15]  R. Poli.  Genetic programming for image analysis , 1996 .

[16]  Terence Soule,et al.  Code growth in genetic programming , 1996 .