Genetic Programming Bloat without Semantics

To investigate the fundamental causes of bloat, six artificial random binary tree search spaces are presented. Fitness is given by program syntax (the genetic programming genotype). GP populations are evolved on both random problems and problems with “building blocks”. These are compared to problems with explicit ineffective code (introns, junk code, inviable code). Our results suggest the entropy random walk explanation of bloat remains viable. The hard building block problem might be used in further studies, e.g. of standard subtree crossover.

[1]  William B. Langdon.  Quadratic Bloat in Genetic Programming , 2000, GECCO.

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

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

[4]  Haynes.  Collective Adaptation: The Exchange of Coding Segments. , 1999, Evolutionary computation.

[5]  Christopher Gathercole.  An investigation of supervised learning in genetic programming , 1998 .

[6]  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).

[7]  Riccardo Poli,et al.  Genetic Programming Bloat with Dynamic Fitness , 1998, EuroGP.

[8]  David E. Goldberg,et al.  Where Does the Good Stuff Go, and Why? How Contextual Semantics Influences Program Structure in Simple Genetic Programming , 1998, EuroGP.

[9]  Riccardo Poli,et al.  Fitness Causes Bloat: Mutation , 1998, EuroGP.

[10]  P. Smith,et al.  Code growth, explicitly defined introns, and alternative selection schemes. , 1998, Evolutionary computation.

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

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

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

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

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

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

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

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

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

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

[21]  Una-May O'Reilly,et al.  An analysis of genetic programming , 1995 .

[22]  Walter Alden Tackett.  Recombination, selection, and the genetic construction of computer programs , 1994 .

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

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

[25]  Peter J. Angeline.  Genetic programming and emergent intelligence , 1994 .

[26]  L. Altenberg.  EMERGENT PHENOMENA IN GENETIC PROGRAMMING , 1994 .

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

[28]  S. K. Park,et al.  Random number generators: good ones are hard to find , 1988, CACM.

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