Analysis of Building Blocks with Numerical Simplification in Genetic Programming

This paper investigates the effect of numerical simplification on building blocks during evolution in genetic programming. The building blocks considered are three level subtrees. We develop a method for encoding building blocks for the analysis. Compared with the canonical genetic programming method, numerical simplification can generate much smaller programs, use much shorter evolutionary training time and achieve comparable effectiveness performance.

[1]  Mengjie Zhang,et al.  Pixel Statistics and False Alarm Area in Genetic Programming for Object Detection , 2003, EvoWorkshops.

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

[3]  Anikó Ekárt,et al.  Shorter Fitness Preserving Genetic Programs , 1999, Artificial Evolution.

[4]  William B. Langdon,et al.  Quadratic Bloat in Genetic Programming , 2000, GECCO.

[5]  Byoung-Tak Zhang,et al.  Balancing Accuracy and Parsimony in Genetic Programming , 1995, Evolutionary Computation.

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

[7]  Riccardo Poli,et al.  Fitness Causes Bloat , 1998 .

[8]  Graham Kendall,et al.  Problem Difficulty and Code Growth in Genetic Programming , 2004, Genetic Programming and Evolvable Machines.

[9]  Peter Nordin,et al.  Complexity Compression and Evolution , 1995, ICGA.

[10]  Riccardo Leardi,et al.  PARVUS: An Extendable Package of Programs for Data Exploration , 1988 .

[11]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[12]  Mark Johnston,et al.  Using Numerical Simplification to Control Bloat in Genetic Programming , 2008, SEAL.

[13]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[14]  Xiaodong Li,et al.  Multi-objective techniques in genetic programming for evolving classifiers , 2005, 2005 IEEE Congress on Evolutionary Computation.

[15]  P. K. Chawdhry,et al.  Soft Computing in Engineering Design and Manufacturing , 1998, Springer London.

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

[17]  Nicholas S. Flann,et al.  Improving the accuracy and robustness of genetic programming through expression simplification , 1996 .

[18]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[19]  Mengjie Zhang,et al.  Algebraic simplification of GP programs during evolution , 2006, GECCO.

[20]  Wolfgang Banzhaf,et al.  A comparison of linear genetic programming and neural networks in medical data mining , 2001, IEEE Trans. Evol. Comput..

[21]  Mark Johnston,et al.  How online simplification affects building blocks in genetic programming , 2009, GECCO.

[22]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[23]  Lisa M LaVange,et al.  Rank Score Tests , 2006, Circulation.

[24]  Gilbert Strang,et al.  The Discrete Cosine Transform , 1999, SIAM Rev..

[25]  Riccardo Poli,et al.  A Simple but Theoretically-Motivated Method to Control Bloat in Genetic Programming , 2003, EuroGP.

[26]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[27]  Ian Witten,et al.  Data Mining , 2000 .

[28]  William D. Smart,et al.  Program Simplification in Genetic Programming for Object Classification , 2005, KES.