Abstract
Inspired by genetic programming (GP), we study iterative algorithms for non-computable tasks and compare them to naive models. This framework justifies many practical standard tricks from GP and also provides complexity lower-bounds which justify the computational cost of GP thanks to the use of Kolmogorov's complexity in bounded time. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Teytaud, O. (2007). Slightly beyond Turing’s computability for studying genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4664 LNCS, pp. 279–290). Springer Verlag. https://doi.org/10.1007/978-3-540-74593-8_24
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