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Darwin simulation version 2002

The picture shows a simple simulation in which a normal population (blue) competes with a mutation (yellow). In each generation the individuals try to multiply. The mutations are stronger than the original population. According to Darwin's theory of evolution, one would eventually reach a population with only yellow individuals (survival of the fittest). Darwin's theori may be written as a simple differential equation. If a mutation has one percent larger probability of being succesful, it will grow according to the series 1, 1.01, 1.02, 1.03, 1.041, 1.051, 1.062,....

In reality, it usually does not grow, but becomes extinguished within a few generations. The reason is that the number of individuals is always an integer. There does not exist anything like 1.02 individuals. Due to statistical fluctuations, the series will become something like 1, 1, 1, 2, 3, 3, 3, 4, 3, 4, 3, 3, 2, 3, 1, 0. As soon as the value happens to be zero, the mutation is gone.

You may download a program that simulates this. For verification purposes, the source code is available here. In this way, you may verify that the code does what I tell.
You could compile it with any C++ compiler.
In addition to those files, there are also user instructions available to download.

Darwin simulation version 2012

The picture to the left shows a simulation in which individuals with two genes compete according to Darwin's mechanism. The original genes (24 bits each) are chosen randomly. During cell division, a gene may mutate. Some mutations cause major advanteges/disadvantages, or even are lethal, while other are almost neutral. Also, many kinds of intermediates are possible.
A certain mutation may give a major advantage if it happen in one kind of gene, while its effect could be much smaller in another kind of gene. The program is able to handle up to 255 species, and within each of them about 26000 variants.

The simulation here includes up to 9000 individuals. The colors indicate genetic properties. To reach that state, 1700 generations had to pass, and 12890 mutations had to happen in the population.

The initial results from simulations indicate that the Darwinian mechanism is too inefficient to explain the state of life as it is on Earth today.

To generate random numbers, the program makes use of the random number generator written by Makoto Matsumoto and Takuji Nishimura (1997).

You may download this program here. For verification purposes, the source codes are available here. In this way, you may verify that the code does what it should.
You could compile it with any C++ compiler.
In addition to those files, there are also user instructions available to download.