Identifying Structural Mechanisms in Standard Genetic Programming

This paper presents a hypothesis about an undiscovered class of mechanisms that exist in standard GP. Rather than being intentionally designed, these mechanisms would be an unintended consequence of using trees as information structures. A model is described that predicts outcomes in GP that would arise solely from such mechanisms. Comparisons with empirical results from GP lend support to the existence of these mechanisms.

[1]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[2]  Riccardo Poli,et al.  The evolution of size and shape , 1999 .

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

[4]  J. K. Kinnear,et al.  Advances in Genetic Programming , 1994 .

[5]  Jason M. Daida,et al.  Visualizing Tree Structures in Genetic Programming , 2003, Genetic Programming and Evolvable Machines.

[6]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[7]  Jason M. Daida,et al.  Analysis of single-node (building) blocks in genetic programming , 1999 .

[8]  Jason M. Daida,et al.  What Makes a Problem GP-Hard? , 2003 .

[9]  L. Sander Diffusion-limited aggregation: A kinetic critical phenomenon? , 2000 .

[10]  J. C. Poggendorf Annalen der Physik und Chemie , 1829 .

[11]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[12]  Donald E. Knuth,et al.  The Art of Computer Programming, Volume I: Fundamental Algorithms, 2nd Edition , 1997 .

[13]  William B. Langdon,et al.  Size Fair and Homologous Tree Crossovers for Tree Genetic Programming , 2000, Genetic Programming and Evolvable Machines.

[14]  Rachel Harrison,et al.  Characterizing a Tunably Difficult Problem in Genetic Programming , 2000, GECCO.

[15]  René Schott,et al.  Random generation of trees - random generators in computer science , 1995 .

[16]  G. Raidl A Hybrid GP Approach for Numerically Robust Symbolic Regression , 2002 .

[17]  Jason M. Daida,et al.  Limits to expression in genetic programming: lattice-aggregate modeling , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[18]  George David Birkhoff The collected mathematical papers , 1909 .

[19]  Jason M. Daida,et al.  What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming , 1999, Genetic Programming and Evolvable Machines.

[20]  David B. Fogel,et al.  Guidelines for a suitable encoding , 1997 .

[21]  Philippe Flajolet,et al.  The Average Height of Binary Trees and Other Simple Trees , 1982, J. Comput. Syst. Sci..

[22]  Una-May O'Reilly,et al.  Genetic Programming II: Automatic Discovery of Reusable Programs. , 1994, Artificial Life.

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

[24]  Donald E. Knuth,et al.  The art of computer programming: V.1.: Fundamental algorithms , 1997 .

[25]  David W. Corne,et al.  A new evolutionary approach to the degree-constrained minimum spanning tree problem , 1999, IEEE Trans. Evol. Comput..

[26]  G. Kirchhoff Ueber die Auflösung der Gleichungen, auf welche man bei der Untersuchung der linearen Vertheilung galvanischer Ströme geführt wird , 1847 .

[27]  Donald Ervin Knuth,et al.  The Art of Computer Programming , 1968 .

[28]  L. Alonso,et al.  Random Generation of Trees , 1995, Springer US.

[29]  Jason M. Daida,et al.  What Makes a Problem GP-Hard? Validating a Hypothesis of Structural Causes , 2003, GECCO.

[30]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[31]  William B. Langdon,et al.  Combining Decision Trees and Neural Networks for Drug Discovery , 2002, EuroGP.