Causality of hierarchical variable length representations

Author

Igel, Christian

Author_Institution

Inst. fur Neuroinf., Ruhr-Univ., Bochum, Germany

fYear

1998

fDate

4-9 May 1998

Firstpage

324

Lastpage

329

Abstract

In this paper, the strong causality of program tree representations is considered. A quantitative, probabilistic causality measure is used in contrast to statistical fitness landscape analysis methods. Although it fails to rank different problems according to their difficulty, it is helpful for choosing the right coding for a given task. The investigation utilizes a metric on the search space called the tree edit distance. Different ways to define such a measure are discussed

Keywords

genetic algorithms; probability; program control structures; programming theory; tree searching; coding; hierarchical variable-length representations; problem difficulty; program tree representations; quantitative probabilistic causality measure; search space metric; statistical fitness landscape analysis; strong causality; tree edit distance; Algorithm design and analysis; Formal languages; Genetic algorithms; Genetic mutations; Genetic programming; Heuristic algorithms; Problem-solving; Simulated annealing; Statistical analysis; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on

Conference_Location

Anchorage, AK

Print_ISBN

0-7803-4869-9

Type

conf

DOI

10.1109/ICEC.1998.699753

Filename

699753