Re: Adaptive learning CPU



On 6 Aug, 23:46, Vend <ven...@xxxxxxxxxxx> wrote:
On 6 Ago, 19:11, spintronic <spintro...@xxxxxxxxxxx> wrote:

On 6 Aug, 01:07, Vend <ven...@xxxxxxxxxxx> wrote:> On Aug 5, 11:09 pm, richardalanforr...@xxxxxxxxxxxxxx wrote:

<snip>

Unless you can find a man-made analogy which reproduces itself, and
introduces modifications as part of that process, you can't.

He would be correct if the system uses evolutionary algorithms or
something of that kind to do the adaptation. Probably it doesn't.

Yes it does!

Do you know what a genetic algorithm is?

http://en.wikipedia.org/wiki/Genetic_algorithm

Yes! Do you?

BTW, under section 12 "Applications" there is this little baby

http://en.wikipedia.org/wiki/Evolvable_hardware

QUOTE;
"In other cases an existing circuit must adapt---i.e.,
modify its configuration---to compensate for faults or perhaps a
changing operational environment"
END QUOTE;


But lets go back to your section 12. Under "Applications"!


"Applications;


1 ) Artificial Creativity
2) Automated design, including research on composite material design
and multi-objective design of automotive components for
crashworthiness, weight savings, and other characteristics.
3) Automated design of mechatronic systems using bond graphs and
genetic programming (NSF).
4) Automated design of industrial equipment using catalogs of exemplar
lever patterns.
5) Calculation of Bound states and Local-density approximations.
6) Chemical kinetics (gas and solid phases)
7) Configuration applications, particularly physics applications of
optimal molecule configurations for particular systems like C60
(buckyballs).
8) Container loading optimization.
9) Code-breaking, using the GA to search large solution spaces of
ciphers for the one correct decryption.
10) Design of water distribution systems.
11) Distributed computer network topologies.
12) Electronic circuit design, known as Evolvable hardware.
13) File allocation for a distributed system.
14) Parallelization of GAs/GPs including use of hierarchical
decomposition of problem domains and design spaces nesting of
irregular shapes using feature matching and GAs.
15) Game Theory Equilibrium Resolution.
16) Learning Robot behavior using Genetic Algorithms.
17) Learning fuzzy rule base using genetic algorithms.
18) Linguistic analysis, including Grammar Induction and other aspects
of Natural Language Processing (NLP) such as word sense
disambiguation.
19) Mobile communications infrastructure optimization.
20) Molecular Structure Optimization (Chemistry).
21) Multiple population topologies and interchange methodologies.
22) Optimisation of data compression systems, for example using
wavelets.
23) Protein folding and protein/ligand docking.
24) Plant floor layout.
25) Representing rational agents in economic models such as the cobweb
model.
26) Scheduling applications, including job-shop scheduling. The
objective being to schedule jobs in a sequence dependent or non-
sequence dependent setup environment in order to maximize the volume
of production while minimizing penalties such as tardiness.
27) Selection of optimal mathematical model to describe biological
systems.
28) Software engineering
29) Solving the machine-component grouping problem required for
cellular manufacturing systems.
30) Tactical asset allocation and international equity strategies.
31) Timetabling problems, such as designing a non-conflicting class
timetable for a large university.
32) Training artificial neural networks when pre-classified training
examples are not readily obtainable (neuroevolution).
33) Traveling Salesman Problem.


You have just given me 33 more examples of "man made - evolution".

Apart from the protein folding one. I dont think that counts as man
made!

.



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