Mapping non-conventional extensions of genetic programming
Author(s) -
William B. Langdon,
Riccardo Poli
Publication year - 2007
Publication title -
natural computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1572-9796
pISSN - 1567-7818
DOI - 10.1007/s11047-007-9044-x
Subject(s) - computer science , genetic programming , lock (firearm) , theoretical computer science , markov chain , variation (astronomy) , theory of computation , turing , programming language , artificial intelligence , machine learning , mechanical engineering , physics , astrophysics , engineering
Conventional genetic programming research excludes memory and iteration. We have begun an extensive analysis of the space through which GP or other unconventional AI approaches search and extend it to consider explicit program stop instructions (T8), including Markov analysis and any time models (T7). We report halting probability, run time and functionality (including entropy of binary functions) of both halting and anytime programs. Irreversible Turing complete program fitness landscapes, even with halt, scale poorly however loops lock-in variation allowing more interesting functions.
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