Is increased diversity in genetic programming beneficial? An analysis of lineage selection

This paper presents an analysis of increased diversity in genetic programming. A selection strategy based on genetic lineages is used to increase genetic diversity. A genetic lineage is defined as the path from an individual to individuals which were created from its genetic material. The method is applied to three problem domains: artificial ant, even-5-parity and symbolic regression of the binomial-3 function. We examine how increased diversity affects problems differently and draw conclusions about the types of diversity which are more important for each problem. Results indicate that diversity in the ant problem helps to overcome deception, while elitism in combination with diversity is likely to benefit the parity and regression problems.

[1]  Larry J. Eshelman,et al.  Crossover's Niche , 1993, ICGA.

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

[3]  Conor Ryan,et al.  Pygmies and civil servants , 1994 .

[4]  F. Oppacher,et al.  Hybridized crossover-based search techniques for program discovery , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[5]  William B. Langdon.  Data structures and genetic programming , 1995 .

[6]  P. Ross,et al.  An adverse interaction between crossover and restricted tree depth in genetic programming , 1996 .

[7]  Maarten Keijzer,et al.  Efficiently representing populations in genetic programming , 1996 .

[8]  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.

[9]  W. Langdon.  The evolution of size in variable length representations , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[10]  B. W.,et al.  Size Fair and Homologous Tree Genetic Programming Crossovers , 1999 .

[11]  K. Chellapilla,et al.  Investigating the influence of depth and degree of genotypic change on fitness in genetic programming , 1999 .

[12]  William B. Langdon,et al.  Size fair and homologous tree genetic programming crossovers , 1999 .

[13]  N. Hopper,et al.  Analysis of genetic diversity through population history , 1999 .

[14]  Dana H. Ballard,et al.  Rooted-tree schemata in genetic programming , 1999 .

[15]  Robert I. McKay,et al.  Fitness Sharing in Genetic Programming , 2000, GECCO.

[16]  Robert I. McKay.  Partial functions in fitness-shared genetic programming , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[17]  Riccardo Poli,et al.  On the Search Properties of Different Crossover Operators in Genetic Programming , 2001 .

[18]  Edwin D. de Jong,et al.  Reducing bloat and promoting diversity using multi-objective methods , 2001 .

[19]  Wolfgang Banzhaf,et al.  Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming , 2002, EuroGP.

[20]  Graham Kendall,et al.  Advanced Population Diversity Measures in Genetic Programming , 2002, PPSN.

[21]  Anikó Ekárt,et al.  Maintaining the Diversity of Genetic Programs , 2002, EuroGP.

[22]  Marcus Hutter,et al.  Fitness uniform selection to preserve genetic diversity , 2001, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  Graham Kendall,et al.  A Survey And Analysis Of Diversity Measures In Genetic Programming , 2002, GECCO.

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

[25]  Sean Luke,et al.  Fighting Bloat with Nonparametric Parsimony Pressure , 2002, PPSN.

[26]  Sean Luke,et al.  Modification Point Depth and Genome Growth in Genetic Programming , 2003, Evolutionary Computation.

[27]  Eddy Flerackers,et al.  Reducing Population Size while Maintaining Diversity , 2003, EuroGP.

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

[29]  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.

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