Hello,




More of my philosophy about my kind of personality and more of my thoughts..

I am a white arab from Morocco, and i think i am smart since i have also invented many scalable algorithms and algorithms..


I think i am highly smart, but i will ask a philosophical question of:


How i can explain my kind of personality ?

I think i am like an inventor or creator, since if you look at my below thoughts you will notice that what i like most is inventing thoughts and inventing algorithms and inventing a philosophy and inventing poems of
Love and poems and inventing proverbs, so as you have just noticed it is
what i am doing in front of you, so my way of doing is mostly
about being creative and inventive that permits me to adapt
efficiently, so look for example how i have quickly invented my
following algorithms here as a logical proof of what i am saying to you:

Scalable reference counting with efficient support for weak references

https://sites.google.com/site/scalable68/scalable-reference-counting-with-efficient-support-for-weak-references

New variants of Scalable RWLocks

https://sites.google.com/site/scalable68/new-variants-of-scalable-rwlocks

Scalable lock that is FIFO fair and starvation-free

https://sites.google.com/site/scalable68/scalable-mlock

And i have also invented many other algorithms too..


And look at my following thoughts so that you notice that
it is the same pattern, i mean that i am the being inventive and creative:

More of my philosophy about my kind of methodology of learning and more
of my thoughts..

I think i am highly smart, and i will explain to you my kind of way of
learning much more clearly:

I don't learn like you are learning, since i have to understand
the architectural ideas more clearly, by inventing them and/or
understanding them much more clearly, and after that this gives a form
to a high level of intelligence that permits me to understand rapidly
and efficiently, so i give you an example, so i have invented many architectural ideas that have given form to my philosophy as a high
level intelligence , and this has allowed me to make you understand more
the weaknesses of the philosophies of other philosophers, so i am doing
it this way for genetic algorithms(read my thoughts about it below)
etc., so i give you an example, so read my following thoughts in the
following web link that i have rapidly invented, and you will notice how
i am doing it by reading them carefully and by noticing my kind of
personality, so here is a part of my philosophy about self-confidence
and about how to be the positive energy and how to be hope so that you
notice how my philosophy is smart:

https://groups.google.com/g/alt.culture.morocco/c/RNxOWBpkHkM


And look here in my following tutorial about Petri Nets how i have
invented a methodology as also architectural ideas that permit to
rapidly model parallel applications from the operators IF and THEN and
OR and AND logic into a Petri Net model, so look at it carefully in my
tutorial here , since i think it is interesting too:

https://sites.google.com/site/scalable68/how-to-analyse-parallel-applications-with-petri-nets

More of my philosophy about the the importance of randomness in
the genetic algorithm and in the evolutionary algorithms and more
of my thoughts..

I think i am highly smart, and i will invite you to read my following
smart thoughts about evolutionary algorithms and artificial intelligence
so that you notice how i am talking about the so important thing that we
call "randomness":

https://groups.google.com/g/alt.culture.morocco/c/joLVchvaCf0


So i think i am highly smart, and notice that i am saying in the above
web link the following about evolutionary algorithms:

"I think that Modern trends in solving tough optimization problems tend
to use evolutionary algorithms and nature-inspired metaheuristic
algorithms, especially those based on swarm intelligence (SI), two major characteristics of modern metaheuristic methods are nature-inspired, and
a balance between randomness and regularity."

So i think that in the genetic algorithm, there is a part that is hard
coded, like selecting the best genes, and i think that it is what
we call regularity, since it is hard coded like that, but there is
a so important thing in that genetic algorithm that we call randomness,
and i think that it is the genetic mutations that happen with a
probability and that give a kind of diversity, so i think that this
genetic mutations are really important, since i can for example
say that if the best genes are the ones that use "reason", so then
reason too can make the people that has the tendency to use reason do
a thing that is against there survival, like going to war when we
feel that there is too much risk, but this going to war can make
the members or people that use reason so that to attack the other enemy
be extinct in a war when they loose a war, and it is the basis of
randomness in a genetic algorithm, since even when there is a war
between for example two Ant colonies, there are some members that do not
make war and that can survive if other are extinct by making war, and i
say it also comes from randomness of the genetics.

More of my philosophy about the other conditions of the genetic
algorithm and about artificial intelligence and more of my thoughts..
.
I think i am highly smart, and i think that the genetic algorithm
is interesting too, but i have to speak about one other most important
thing about the genetic algorithm, so i will ask a philosophical
question about it:

Since as i just said previously, read it below, that a good genetic
algorithm has to efficiently balance between global(exploration) and local(exploitation) search , but how can you be sure that you have found
a global optimum ?

I think i am smart, and i will say that it also depends on the kind of
problem, so if for example we have a minimization problem, you can
rerun a number of times the genetic algorithm so that to select the best minimum among all the results and you can also give more time to
the exploration so that to find the a better result, also you have to
know that the genetic algorithm can be more elitist in the crossover
steps, but i think that this kind of Elitism can has the tendency to not efficiently higher the average best of the average members of the
population, so then it depends on wich problem you want to use the
genetic algorithm, also i think that the genetic algorithm is
a model that explains from where comes humans, since i also think
that the genetic mutations of humans, that happens with a probability,
has also not only come from the inside body from the chromosomes and
genes, but they also were the result of solar storms that, as has said
NASA, that may have been key to life on Earth, read here so that to
notice it:

https://www.nasa.gov/feature/goddard/2016/nasa-solar-storms-may-have-been-key-to-life-on-earth

Read my previous thoughts:

More of my philosophy about the genetic algorithm and about artificial intelligence and more of my thoughts..

I think i am highly smart, and as you have just noticed, i have just
talked yesterday about the distributed intelligence and collective intelligence, and i invite you to read it in my below thoughts, and
today i will invent more of my thoughts about the genetic algorithm, so
i will ask the following philosophical question about the genetic algorithm:

Is the genetic algorithm a brute-force search and if it is
not, how is it different than the brute-force search ?

so i have just quickly took a look at some example of a minimization
problem with a genetic algorithm, and i think that the genetic algorithm
is not a brute-force search, since i think that when in a minimization
problem with a genetic algorithm you do a crossover, also called
recombination, that is a genetic operator used to combine the genetic information of two parents to generate new offspring, the genetic
algorithm has this tendency to also explore locally and we call it exploitation, and when the genetic algorithm does genetical mutations
with a level of probability, the genetic algorithm has this tendency to
explore globally and we call it exploration, so i think a good genetic algorithm is the one that balance efficiently exploration and
exploitation so that to avoid premature convergence, and
notice that when you explore locally and globally you can do it with
a bigger population that makes it search faster, so it is is why i think
the genetic algorithm has this kind of patterns that makes it a much
better search than brute-force search. And so that to know more about
this kind of artificial intelligence , i invite you to read my following thoughts in the following web link about evolutionary algorithms and
artificial intelligence so that to understand more:

https://groups.google.com/g/alt.culture.morocco/c/joLVchvaCf0


More of my philosophy about the analytic queueing-circuit analyzer PDQ
and more of my thoughts..

I am a white arab from Morocco, and i think i am smart since i have also invented many scalable algorithms and algorithms..


I think i am highly smart, since i have also passed two certified IQ
tests and i have scored above 115 IQ, so as you have just noticed i have
just talked about timesharing and coroutines in the following link:

https://groups.google.com/g/alt.culture.morocco/c/tN_O-AgkGjY

And i have just talked about Markov chains in mathematics and about
Petri Nets here:

https://groups.google.com/g/soc.culture.quebec/c/LPE4zR-BqHY

Now i will talk about my PDQ for Delphi and Freepascal:

This is a port by Amine Moulay Ramdane of PDQ version 6.2.0 to Delphi
on Windows and to Freepascal on both Windows and Linux, i have also
provided you with two demos, one queuing MM1 demo, and another Jackson
network demo. Also i have provided you with my HTML tutorial on how to
solve analytically the Jackson network problem provided to you as a PDQ
demo.

PDQ is an analytic queueing-circuit analyzer made freely available under MIT/X11 license from: http://www.perfdynamics.com/Tools/PDQ.html

You can download it from my website here:

https://sites.google.com/site/scalable68/pdq-for-delphi-and-freepascal

And here is an example in Delphi and Freepascal of the Jackson network
demo that i have included inside the zip file(and you can look
at my mathematical modeling of it here in my website: https://sites.google.com/site/scalable68/jackson-network-problem):

---

program test_network;


uses pdq64,LinSys;


type router1 = array of pansichar;
servTime1 = array of double;
visitRatios1 = array of double;
serviceDemands1 = array of double;
var

A, b, x : TMatrix;
arrivRate:double;
work:pansichar;
router:router1;
servTime:servTime1;
visitRatios:visitRatios1;
serviceDemands:serviceDemands1;
i:integer;

begin

setlength(router,3);
setlength(servTime,3);
setlength(visitRatios,3);
setlength(serviceDemands,3);


arrivRate := 0.50;
work:='Traffic';

router[0]:='Router1';
router[1]:='Router2';
router[2]:='Router3';

servTime[0]:=1.0;
servTime[1]:=2.0;
servTime[2]:=1.0;

A := TMatrix.Create (3,3);
b := TMatrix.Create (3,1);
x := TMatrix.Create (3,1);

A[1,1] := 1.0; A[1,2] := 0.0; A[1,3] := -0.2;
A[2,1] := -0.5; A[2,2] := 1.0; A[2,3] := 0.0;
A[3,1] := -0.5; A[3,2] := -0.8; A[3,3] := 1.0;

b[1,1] := 0.5; b[2,1] := 0.0; b[3,1] := 0.0;

LinSys.gauss(A, b, x);

visitRatios[0]:=x[1,1]/arrivRate;
visitRatios[1