🚀 go-pugleaf

In article <1jlmb2INNjp4@gaia.ucs.orst.edu> choup@ava.bcc.orst.edu (Ping Chou) writes:
>        Don't you think you may be able to discover new algorithms from
>studying genetics?

>        Then please, give youself a chance, take a look at any modern
>genetics text book, and be a cross-discipline expert.
>
>--Ping
>
>        BTW, I would appreciate any reasonable arguments because I'm trying
>to learn ai-genetic from the genetics side.

Well, I'm glad to hear that we have genetics experts reading this
newsgroup. I've read the excellent book "Evolutionary Genetics" by John
Maynard Smith (OUP, 1989), and although I learned a lot about genetics,
its really not the same as looking at things from a GA viewpoint. A
geneticist is more concerned with `how things work in nature', while a
GA person wants to extract the _essential_ principles, and apply them
as directly as possible.

Natural genetic systems include various features which are not relevant
in the GA world. For example, there are many different ways in which
errors can occur in the process of DNA replication, and nature has
evolved many different mechanisms to repair these errors. Does this
mean that in a GA we must slavishly copy this process - ie introduce
lots of mutations, using different mechanisms, then repair most of
them?  No - what we do is extract the _essential_ principle that, after
replication has been completed, there will be a small chance that a
number of mutations remain unrepaired.

There may be yet other features of natural genetic systems which,
although essential in a biological context, in fact have nothing to do
with the _evolutionary_ potential of the system. Furthermore, there may
be features which, like the appendix, once performed a function, but
now do nothing.

Of course it can be very difficult to tell what category any particular
natural genetic feature falls into. I would particularly like to ask
the genticists out there for their opinions on the following two
topics:


Inversion. 

We know this takes place (occasionally), but to what extent is it
responsible for evolution? Holland, and other early researchers,
included inversion in their GAs, but more recently it has been notable
by its absence. Is this because nobody has been able to quantify its
advantages? Obviously it has some role to play in the evolution of the
karyotype (chromosome structure), but maybe this type of evolution is
occurring at rates which are orders of magnitude slower than evolution
of the genotype -- in which case it is probably irrelevant for present
day GAs.

Introns

These are redundant sections within genes. Present-day natural genes do
not work properly if introns are artificially removed. This shows that
chromosomes have evolved so that introns are essential _now_, but this
does not prove that it _had_ to happen this way. Were they essential in
the past? Do they add evolutionary potential, perhaps? They can alter
the crossover probability with respect to the operational part of the
gene (see Levenick, James, "Inserting introns improves genetic
algorithm success rate: taking a cue from biology", ICGA-91) - but a
similar effect might be achieved with mating tags. Is there something
special about introns as such which means that they ought to be
included in GA representations?


No doubt some of my reasoning is wrong here. But as an engineer
with no qualifications in biology, I would be grateful for some learned
discussion on these points.
--

David Beasley                          (David.Beasley@cm.cf.ac.uk)    
Department of Computing Mathematics 
University of Wales College of Cardiff                          __o
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