Re: virus: Level Three-Belief and Utility.
Fri, 1 Nov 1996 09:22:59 -0600 (CST)

On Mon, 30 Sep 1996, David Leeper wrote:

> Kevin O'Connor, Richard Brodie,
> > >> If you imagine the mind as a landscape of
> > >> peaks and valleys, can you see how a Level-2 mind could easily get
> > >> caught in a valley or a hillock?
> > >
> > >Replicators, including memes, have an unsupased ability to
> > >not get caught in a "valley or a hillock".
> > >
> Kevin O'Connor wrote:
> > Replicators also tend to reach and get stuck on sub-optimal peaks.
> Richard Brodie wrote:
> > That statement is in direct opposition to what scientists are saying
> > about replicators. Dawkins, Dennett, and even Gould agree that getting
> > caught at local maxima is an inherent quality of evolution by natural
> > selection. That is why, for instance, our eyes have nerves coming out
> > the front instead of the back.


> 1) If evolution got stuck, it wouldn't be evolution. I challange you
> to find _any_ evolutionist of repute who says evolution gets stuck.
> Name dropping doesn't count, let's see something solid.

You actually expect quotes from high priests that denounce their own
religion to exist?

> 2) If you imagine the mind as a landscape of peaks and valleys and mutations
> occuring which randomly move the replicators around the landscape, can you
> see how it is impossible to get stuck in a local optimum? Mutations are
> random, they don't seek out global or local optima, they just move their
> replicators around randomly.

Local maxima: points where the gradient of the evaluation function for
relative survival is 0, and all local orthonormal frames have the
survival function decreasing on all basis vectors. [This is a fancy way
of saying 'locally optimal solutions that Natural selection ends up at'.]

If the mutations don't go far enough, they will wipe before finding any
nearby [improved] local maximum.

> 3) One of the most useful situations for writing Evolution-style computer
> programs is when standard approaches get stuck in local optima. Why?
> Evolutionary-style programs do not get stuck in local-optima. I refer
> you to "An Introduction On Genetic Algorithms". It's written by the
> brilliant Melanie Mitchell, published by MIT Press and is available at
> the low cost of $30.00.

Having read up on standard approaches: even isocoding by hand gives
superior results. [This is a highly nonstandard approach.] It would not
surprise me that genetic algorithms can do better than incompetent
standard approaches.

> 4) There are exceptions to 3). For example: W. Daniel Hillis and his
> experiments with using GAs to discover sorting networks. However, his
> experiments had a limit on 1) The number of generations allowed, 2) The
> time allowed for the total experiment and 3) The processing power
> available to throw at the problem. Real world Evolution suffers none
> of these limitations.

Under suitable assumptions, such as many-world interpretation of quantum
mechanics, or the steady-state universe.

/ Towards the conversion of data into information....
/ Kenneth Boyd