Hmm, well if you just use mutations then this becomes a greedy
algorithm which will either get stuck in local optima or take
close-to-forever

If you use crossover operators or EDA-style probabilistic models then
things become potentially tractable, but only under appropriate
assumptions regarding the fitness landscape... right?

On Tue, Mar 23, 2021 at 2:15 PM James Bowery <[email protected]> wrote:
>
> 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.
> Artificial General Intelligence List / AGI / see discussions + participants + 
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-- 
Ben Goertzel, PhD
http://goertzel.org

“He not busy being born is busy dying" -- Bob Dylan

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