Abstract
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) and any time models (T7). We report halting probability, run time and functionality (including entropy of binary functions) of both halting and anytime programs. Turing complete program fitness landscapes, even with halt, scale poorly. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Langdon, W. B. (2006). Mapping non-conventional extensions of genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4135 LNCS, pp. 166–180). Springer Verlag. https://doi.org/10.1007/11839132_14
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