[continued from previous message]

domino effect of cascading changes...and eventually leaving you with
an unmaintainable mess of spaghetti code. So when teams write code,
they can keep their software designs simple by creating software
designs based on small, self-contained units (like classes, modules,
services, etc.) that do only one thing; this helps avoid the domino
effect.

3- Instead of creating one big design at the beginning of the project
that covers all of the requirements, agile architects use incremental
design, which involves techniques that allow them to design a system
that is not just complete, but also easy for the team to modify as
the project changes.

4- When in agile a team breaks a project into phases, it’s called
incremental development. An incremental process is one in which
software is built and delivered in pieces. Each piece, or increment,
represents a complete subset of functionality. The increment may be
either small or large, perhaps ranging from just a system’s login
screen on the small end to a highly flexible set of data management
screens. Each increment is fully coded Sprints, Planning, and
Retrospectives.

5- And an iterative process in agile is one that makes progress through
successive refinement. A development team takes a first cut
at a system, knowing it is incomplete or weak in some (perhaps many)
areas. They then iteratively refine those areas until the product is
satisfactory. With each iteration the software is improved through
the addition of greater detail.

More of philosophy about Democracy and the Evolutionary Design methodology..

I will make a logical analogy between software projects and Democracy,
first i will say that because of the today big complexity of software
projects, so the "requirements" of those complex software projects are
not clear and a lot could change in them, so this is
why we are using an Evolutionary Design methodology with different tools
such as Unit Testing, Test Driven Development, Design Patterns,
Continuous Integration, Domain Driven Design, but we have to notice
carefully that an important thing in Evolutionary Design methodology is
that when those complex software projects grow, we have first to
normalize there growth by ensuring that the complex software projects
grow "nicely" and "balanced" by using standards, and second we have to
optimize growth of the complex software projects by balancing between
the criteria of the easy to change the complex software projects and the performance of the complex software projects, and third you have to
maximize the growth of the complex software projects by making the most
out of each optimization, and i think that by logical analogy we can
notice that in Democracy we have also to normalize the growth by not
allowing "extremism" or extremist ideologies that hurt Democracy, and we
have also to optimize Democracy by for example well balancing between "performance" of the society and in the Democracy and the "reliability"
of helping others like the weakest members of the society among the
people that of course respect the laws.


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

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

I think i am highly smart, 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 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

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 student performance and about artificial intelligence and more of my thoughts..


I have just read the following interesting article from McKinsey, and
i invite you to read it carefully:

Drivers of student performance: Asia insights

https://www.mckinsey.com/industries/education/our-insights/drivers-of-student-performance-asia-insights

And i think i am smart, and i think that the following factors in the above article that influence student performance are not so difficult to implement:

1- Students who receive a blend of inquiry-based and teacher-directed instruction have the best outcomes

2- School-based technology yields the best results when placed in the
hands of teachers

3- Early childhood education has a positive impact on student scores,
but the quality and type of care is important

But i think that the factor that is tricky to implement (since it needs good smartness) is good motivation calibration that permits to score 8 to 14 percent higher on the science test than poorly calibrated one, and the high self-identified motivation
that permits to score 6 to 8 percent higher.




Thank you,
Amine Moulay Ramdane.

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