Investigating the influence of depth and degree of genotypic change on fitness in genetic programming

In this paper we investigate the influence of (a) the amount of variation generated in the genotype and (b) the depth of application of variation operators on the offspring fitness in genetic programming. Simulation results on three common test problems indicate that for certain features of the fitness distribution the location of the variation may play as important a role as the choice of the applied operators.

[1]  David Sankoff,et al.  Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison , 1983 .

[2]  Kaizhong Zhang,et al.  Simple Fast Algorithms for the Editing Distance Between Trees and Related Problems , 1989, SIAM J. Comput..

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

[4]  Una-May O'Reilly,et al.  Program Search with a Hierarchical Variable Lenght Representation: Genetic Programming, Simulated Annealing and Hill Climbing , 1994, PPSN.

[5]  John J. Grefenstette Predictive Models Using Fitness Distributions of Genetic Operators , 1994, FOGA.

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

[7]  Justinian P. Rosca,et al.  Causality in Genetic Programming , 1995, International Conference on Genetic Algorithms.

[8]  David B. Fogel,et al.  Using fitness distributions to design more efficient evolutionary computations , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[9]  Bernhard Sendhoff,et al.  A Condition for the Genotype-Phenotype Mapping: Causality , 1997, ICGA.

[10]  Una-May O’Reilly Using a distance metric on genetic programs to understand genetic operators , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[11]  Kumar Chellapilla,et al.  Evolving computer programs without subtree crossover , 1997, IEEE Trans. Evol. Comput..

[12]  John Beidler,et al.  Data Structures and Algorithms , 1996, Wiley Encyclopedia of Computer Science and Engineering.

[13]  H. Iba,et al.  Depth-dependent crossover for genetic programming , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[14]  C. Igel Causality of hierarchical variable length representations , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

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

[16]  Christian Igel,et al.  Fitness distributions: tools for designing efficient evolutionary computations , 1999 .

[17]  Shane S. Sturrock,et al.  Time Warps, String Edits, and Macromolecules – The Theory and Practice of Sequence Comparison . David Sankoff and Joseph Kruskal. ISBN 1-57586-217-4. Price £13.95 (US$22·95). , 2000 .