Evolutionary program synthesis requires a fitness/cost function which, in the case of Solomonoff Induction, can be approximated by the size of the program that outputs exactly the currently known observations. The obvious problem with this approach is that of all algorithms, only a disappearingly small fraction will output exactly the known observations.
Reduce the space by starting with the known observations as an executable literal -- say by putting it in quotes for evaluation -- and use a reversible programming language with its algebraic identities as mutations -- treating the "discarded" bits (inherent in reversible algorithms) as needing compression as well. In the limit, this can be represented as a directed cyclic graph of reversible logic gates which will tend to configure in such a way as to make the "heat" bits highly compressible (and in the limit, all 0s or all 1s). This originally occurred to me prior to the announcement of the Hutter Prize back in 2006 but Matt had some argument debunking this approach. PS: It was rather ironic that one of the first and most vocal critics of The Hutter Prize was the inventor of the Kayak reversible programming language <http://esoteric.sange.fi/essie2/download/kayak/kayak.html>. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf8bb7754cbb517a4-M6948aeb5c0dc1cb68f860038 Delivery options: https://agi.topicbox.com/groups/agi/subscription
