New theory on the origin of primates

There you go again, claiming to have calculated how many ‘generations’ a species need to evolve to another. How did you calculate this?

This is a useless postulation since you didn’t even take into account the size of the population. Obviously a species with a population of millions has more potential to evolve than a species with a population of a few dozen.

I haven’t calculated.

I’m simply saying, what’s the least possible number of generations necessary to see a new feature.

If we think this is hundreds of thousands of generations, say 100 to 200 thousand, but mammals have only been around for 1,000 generations, then there has not been enough time.

I’m simply saying, what’s the least possible number of generations necessary to see a new feature.

It's evolution; things are in constant flux.

You're not going to be able to see a "new feature", since whatever you're looking at has been making slow incremental changes over millions of years.

Biology doesn't work like an assembly line.

But at some point there as to be a “new feature”, or there is no evolution, the species are unchanging.

If there's no evolution, then where do new species come from? Do they appear out of thin air?

Even Intelligent Design proponents recognize common descent.

Bush did it?

I guess every animal comes from it’s parents.

Engineers and scientists, despite what they have been habituated to say, could never design anything by the process of evolution; it is mathematically impossible.

It's done all the time. Genetic Algorithms and Evolutionary Programming have been around for decades now, and are even used by major coporations to solve real world problems. See, for instance:

Genetic Algorithms and Evolutionary Computation (Talk Origins)

This article gives numerous examples. Just a few:

As cited in Begley and Beals 1995, Texas Instruments used a genetic algorithm to optimize the layout of components on a computer chip, placing structures so as to minimize the overall area and create the smallest chip possible. Using a connection strategy that no human had thought of, the GA came up with a design that took 18% less space.
Beasley, Sonander and Havelock 2001 used a GA to schedule airport landings at London Heathrow, the United Kingdom's busiest airport. This is a multiobjective problem that involves, among other things, minimizing delays and maximizing number of flights while maintaining adequate separation distances between planes (air vortices that form in a plane's wake can be dangerous to another flying too closely behind). When compared to actual schedules from a busy period at the airport, the GA was able to reduce average wait time by 2-5%, equating to one to three extra flights taking off and landing per hour - a significant improvement. However, even greater improvements have been achieved: as reported in Wired 2002, major international airports and airlines such as Heathrow, Toronto, Sydney, Las Vegas, San Francisco, America West Airlines, AeroMexico, and Delta Airlines are using genetic algorithms to schedule takeoffs, landings, maintenance and other tasks, in the form of Ascent Technology's SmartAirport Operations Center software (see http://www.ascent.com/faq.html). Breeding and mutating solutions in the form of schedules that incorporate thousands of variables, "Ascent beats humans hands-down, raising productivity by up to 30 percent at every airport where it's been implemented."
As reported in Lemley 2001, United Distillers and Vintners, a Scottish company that is the largest and most profitable spirits distributor in the world and accounts for over one-third of global grain whiskey production, uses a genetic algorithm to manage its inventory and supply. This is a daunting task, requiring the efficient storage and distribution of over 7 million barrels containing 60 distinct recipes among a vast system of warehouses and distilleries, depending on a multitude of factors such as age, malt number, wood type and market conditions. Previously, coordinating this complex flow of supply and demand required five full-time employees. Today, a few keystrokes on a computer instruct a genetic algorithm to generate a new schedule each week, and warehouse efficiency has nearly doubled.
This technique [using genetic algorithms to find optimal routing paths in telecommunications networks] has found real-world applications for similar purposes, as reported in Begley and Beals 1995. The telecommunications company U.S. West (now merged with Qwest) was faced with the task of laying a network of fiber-optic cable. Until recently, the problem of designing the network to minimize the total length of cable laid was solved by an experienced engineer; now the company uses a genetic algorithm to perform the task automatically. The results: "Design time for new networks has fallen from two months to two days and saves US West $1 million to $10 million each" (p.70).

Genetic algorithms not only sometimes hit upon solutions no human designer ever thought of (since they have no biases about what is good or bad design, or what is likely or unlikely to work) but, in at least one instance described in this article, produced an effective and superior result, that humans could not even explain in retrospect!:

A field-programmable gate array, or FPGA for short, is a special type of circuit board with an array of logic cells, each of which can act as any type of logic gate, connected by flexible interlinks which can connect cells. Both of these functions are controlled by software, so merely by loading a special program into the board, it can be altered on the fly to perform the functions of any one of a vast variety of hardware devices.

Dr. Adrian Thompson has exploited this device, in conjunction with the principles of evolution, to produce a prototype voice-recognition circuit that can distinguish between and respond to spoken commands using only 37 logic gates - a task that would have been considered impossible for any human engineer. He generated random bit strings of 0s and 1s and used them as configurations for the FPGA, selecting the fittest individuals from each generation, reproducing and randomly mutating them, swapping sections of their code and passing them on to another round of selection. His goal was to evolve a device that could at first discriminate between tones of different frequencies (1 and 10 kilohertz), then distinguish between the spoken words "go" and "stop".

This aim was achieved within 3000 generations, but the success was even greater than had been anticipated. The evolved system uses far fewer cells than anything a human engineer could have designed, and it does not even need the most critical component of human-built systems - a clock. How does it work? Thompson has no idea, though he has traced the input signal through a complex arrangement of feedback loops within the evolved circuit. In fact, out of the 37 logic gates the final product uses, five of them are not even connected to the rest of the circuit in any way - yet if their power supply is removed, the circuit stops working. It seems that evolution has exploited some subtle electromagnetic effect of these cells to come up with its solution, yet the exact workings of the complex and intricate evolved structure remain a mystery (Davidson 1997).