A Simple but Theoretically-Motivated Method to Control Bloat in Genetic Programming

This paper presents a simple method to control bloat which is based on the idea of strategically and dynamically creating fitness "holes" in the fitness landscape which repel the population. In particular we create holes by zeroing the fitness of a certain proportion of the offspring that have above average length. Unlike other methods where all individuals are penalised when length constraints are violated, here we randomly penalise only a fixed proportion of all the constraint-violating offspring. The paper describes the theoretical foundation for this method and reports the results of its empirical validation with two relatively hard test problems, which has confirmed the effectiveness of the approach.

[1]  Riccardo Poli,et al.  A Schema Theory Analysis of the Evolution of Size in Genetic Programming with Linear Representations , 2001, EuroGP.

[2]  Peter J. Fleming,et al.  Multiobjective Genetic Programming: A Nonlinear System Identification Application , 1997 .

[3]  Nicholas Freitag McPhee,et al.  Accurate Replication in Genetic Programming , 1995, ICGA.

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

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

[6]  William B. Langdon,et al.  Genetic Programming Bloat without Semantics , 2000, PPSN.

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

[8]  T. Soule,et al.  Code Size and Depth Flows in Genetic Programming , 1997 .

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

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

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

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

[13]  P. Nordin,et al.  Explicitly defined introns and destructive crossover in genetic programming , 1996 .

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

[15]  Hitoshi Iba,et al.  Genetic programming using a minimum description length principle , 1994 .

[16]  Anikó Ekárt,et al.  Selection Based on the Pareto Nondomination Criterion for Controlling Code Growth in Genetic Programming , 2001, Genetic Programming and Evolvable Machines.

[17]  Terence Soule,et al.  Effects of Code Growth and Parsimony Pressure on Populations in Genetic Programming , 1998, Evolutionary Computation.

[18]  Tobias Blickle,et al.  Evolving Compact Solutions in Genetic Programming: A Case Study , 1996, PPSN.

[19]  ProgrammingJustinian P. RoscaComputer Analysis of Complexity Drift in Genetic , 1997 .

[20]  Riccardo Poli,et al.  General Schema Theory for Genetic Programming with Subtree-Swapping Crossover , 2001, EuroGP.

[21]  John R. Koza,et al.  A genetic approach to the truck backer upper problem and the inter-twined spiral problem , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

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

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

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

[25]  Riccardo Poli,et al.  Why Ants are Hard , 1998 .

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

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

[28]  W. Langdon An Analysis of the MAX Problem in Genetic Programming , 1997 .

[29]  William B. Langdon,et al.  Scaling of Program Fitness Spaces , 1999, Evolutionary Computation.

[30]  Terence Soule,et al.  An Analysis of the Causes of Code Growth in Genetic Programming , 2002, Genetic Programming and Evolvable Machines.

[31]  Terence Soule,et al.  Removal bias: a new cause of code growth in tree based evolutionary programming , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).