Workshop with LIVE TRADES

if you are one, be ready to hatch the eggs and raise the babies!!!

Beta (as in Son) the country needs you (in CBI, RAW or Military Intelligence)!

How about giving him a high paying carrer in Mossad!!

Its about some fellow who posted some stuff here about some brilliant TA. Otherwise nobody cares (except you perhaps). So its not about shouting. Its weeding out crap. And yeah, if this was the typical Indian way, all vindis and pindis would be on the roadside out of business long ago. Unfortunately most people tend to accept disguised crap at face value.

Ah the boss is here! Yeah missed that, but then it would be too physical, not enjoying life on a gaddi and submitting reports to "Ma'm" without moving your butt.

Logic,u vanished?

The 'entertainment value' is the motivator itself!

Few things collected from here & there & being shared here the intention is to understand few things as straight forward as possible in subsequent posts.

What Is Optimization?
Optimization is the process of trying to find the best solution to a
problem that may have many possible solutions. Most problems
involve many variables that interact based on given formulas and
constraints. For example, a company may have three manufacturing
plants, each manufacturing different quantities of different goods.
Given the cost for each plant to produce each good, the costs for each
plant to ship to each store, and the limitations of each plant, what is the
optimal way to adequately meet the demand of local retail stores while
minimizing the transportation costs? This is the sort of question that
optimization tools are designed to answer.

From looking at some Report (posted by Ajay)we have to consider ;
What type of Constraints used := Hard ///(or conditions that must be met for a solution to be valid)/// Soft ( or conditions which we would like to be met
as much as possible, but which we may be willing to compromise for a big
improvement in fitness)/// Range ( or minimum and maximum possible values
for each of the variant's ranges entered).
We do not know whether a PENALTY Functions on specific soft constraints used or not.
The population size:= how many organisms (or complete sets
of variables) used & stored in memory at any given time.
The Random Number Seed:=
When the same seed value is used, an optimization will generate the exact same answers for the same model as long as there are no random elements in
the model and the model has not been modified.

Why ? Optimization needs constant monitoring.
For larger or more complex problems, you may be able to write specific, customized algorithms to get good results, but this may require a lot of research and development. Even then, the resulting program would require
modification each time your model changed.

Here enters Genetic Algorithm:=
Genetic algorithms mimic Darwinian principles of natural selection by creating an environment where hundreds of possible solutions to a problem can compete with one another and only the “fittest” survive. Just as in biological evolution,each solution can pass along its good “genes” through “offspring” solutions so that the entire population of solutions will continue to
evolve better solutions.

Our Objective :=
Grouping of variables that produces the highest profits, the lowest risk, or the most Profits from deployment the least amount of available Resource.

Crossover and Mutation:= (GA terms)
One of the most difficult problems with searching for optimal solutions,
when your problem has seemingly endless possibilities, is in
determining where to focus your energy. In other words, how much
computational time should be devoted to looking in new areas of the
“solution space”, and how much time should be devoted to fine-tuning
the solutions in our population that have already proven to be pretty
good?
A big part of the genetic algorithm success has been attributed to its
ability to preserve this balance inherently. The structure of the GA
allows good solutions to “breed”, but also keeps “less fit” organisms
around to maintain diversity in the hopes that maybe a latent “gene”
will prove important to the final solution.

A higher mutation rate simply means that more mutations or random
“gene” values will be introduced into the population.

If all the data of the optimal solution was somewhere in the population,
then the crossover operator alone would be enough to eventually piece
together the solution. Mutation has proven to be a powerful force in the
biological world for many of the same reasons that it is needed in a
genetic algorithm: it is vital to maintaining a diverse population of
individual organisms, thereby preventing the population from
becoming too rigid, and unable to adapt to a dynamic environment. As
in a genetic algorithm, it is often the genetic mutations in animals which
eventually lead to the development of critical new functions.

Auto-Mutation:= (Of a GA software)

By selecting Auto in the Mutation rate field,
users of Professional and Industrial field can select automutation
rate adjustment. Auto-mutation rate adjustment allows
to increase the mutation rate automatically when an organism
"ages" significantly; that is, it has remained in place over an extended
number of trials. For many models, especially where the optimal
mutation rate is not known, selecting Auto can give better results faster.

So for Better Optimization we can take recourse of GA (which Jesse is more
knowledgeable ,& obviously Ajay is more knowledgable than me.)

Let us stop squabbling & start Learning.

Even in the ongoing Twenty 20 match Peoples are using the
"Resource Allocation " or "Assignment of Tasks" of Genetic Algorithm s/w to find the right combinations.