Asking for some neuronal power

Hi guyz

Some time ago, a fag posted a thread about evolutionary programming. Well, other fag answered saying "Read Maturana and read Ashby".

I listened to him. Maturana is a cool guy, very interesting ideas, but the Ashby's book got me.

I've only read the 1st chapter. The book is a little dense for me. But, I am a little bored of making SQL queries and fixing bash scripts at work. So, i think it could be an interesting quest to develop a "cybernetic library", so i can learn from the book making programs instead of lame excercises...

So, who want's to help me?

I know perl and C, and also some useless and less interesting stuff. The 1st chapter of Asbhys book is very interesting and lays the basic components, like the transform, the transformation, operands and such.

I mostly need "conceptual" help on how to model such primitives.

Help me!

the book = http://pespmc1.vub.ac.be/books/introcyb.pdf

>>1
>>10
You could try looking at some of the neuroevolution literature on how to develop genetic encoding schemes that minimize the ratio of genetic information to phenotypic information.

Bear with me.

The amount of information that a genetic system can maintain is limited by mutational pressure and by bounds on selective pressure. mutational pressure scales linearly with the length of the genome and has an entropic (destructive) influence on the information in the genome. Meanwhile, selective pressure, which acts to hold mutational pressure at bay, is invariant to genome size and is bounded, over time, by population demographics. As selective pressure increases, survivability in the population goes below levels needed to sustain the size of the population. If such elevated levels of selective pressure persist over a certain period of time, extinction occurs. Thus, genome length must remain small enough that mutational pressure does is not stronger than can be counteracted by sustainable levels of selective pressure.

The relevant implication of this is that in order for a genome to evolve increasingly complex phenotypes, one bit of genetic information must code for increasingly large amounts of phenotypic information. Such encoding is referred to as "implicit encoding." Implicit encoding is apparent in the human brain, which, even in a prenatal state, contains more information than an entire human genome.

Implicit encoding is an ongoing area of research in the area of (artificial) neuroevolution, which is a form of machine learning.

see especially the sections on implicit encoding.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.8975&rep=rep1&type=pdf
http://infoscience.epfl.ch/record/112676/files/FloreanoDuerrMattiussi2008.pdf