Re: (meta-)meta-design (was: Coding a Transhuman AI 2.0a)

From: Anders Sandberg (
Date: Tue May 23 2000 - 09:45:01 MDT

The problems with evolving GAs in my experience (I have played a
little bit with it, and evolving neural networks and learning rules
for them) is that in general convergence is slow. Fitness is a
stochastic variable you need to find the expectation of when you try
to evaluate the fitness of the meta-population, but that takes a lot
of repeat evaluations - which is the costly part. Without getting rid
of the noise the meta-level GAs will instead have a very random
fitness landscape, and convergence seems uncertain.

So if you have a population of N individuals in the meta-population
and M individuals in the population and do k evaluations for each
individual in the population, you have kNM evaluations to do per
generation in the meta-population. Doing it on higher meta-levels is
even worse, even small populations quickly become very slow. Time to
start running clusters...

Anders Sandberg                                      Towards Ascension!                  
GCS/M/S/O d++ -p+ c++++ !l u+ e++ m++ s+/+ n--- h+/* f+ g+ w++ t+ r+ !y

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