## Success in the Real World

In the known real world the success of a system is dependent on his ability to intake enough energy to compensate for the consumption of energy in a 'sufficient' manner. Additionally there should be some offspring to compensate for the finite duration of a system . Thus we have the condition that and 5.3. Thus there are some 'real' parameters which constrain the success of a real system.

In a simulated world one has to provide such constraining parameters in some analogous way.

Therefore the world as the world of the working population as well as every element (= system) provides a basic mechanism for energy intake, energy consumption, offspring generation as well as process termination.

Based on this minimal framework of operations one can observe the success or failure of a working population only by 'counting' the number of offspring as a 'hint' for the 'quality' of the working system.

Here it is helpful to distinguish between the 'genotype' and the 'phenotype' of a system. Real systems occur in two different modes. While the genotype gives only the 'instructions' (the 'blueprint') for the phenotype of a system, the phenotype does the observable work in the real world. Thus a phenotype represents a system which has some input from the world as well as it can have some output. The transition from the genotype to the phenotype is realized by some growth-function . Thus we have:

 (5.22) (5.23) (5.24) (5.25) (5.26) (5.27) (5.28) (5.29) (5.30) (5.31) (5.32) (5.33) (5.34) (5.35) (5.36) (5.37) (5.38) (5.39) (5.40)

Thus a world consists of an environment with properties located at positions . A position can be a set of vectors . The distribution of properties at positions can change . Properties can be objects , working genotypes as well as phenotypes . With the function one can generate phenotypes out of genotypes. The function can enable a working phenotype to transfer objects, which have been 'picked up' from the 'intake area', into energy . A phenotype can be moved within the distance of MoveArea. Two phenotypes can create a new working genotype by if they are 'close enough'. There is also a death-function which deletes a working system if the lifespan has been ended. If within the 'normal' lifespan the energy drops below zero a death will also happen with . The function computes for every set of indexed genes and phenotypes for some duration a number as 'fitness value'. The values of the eval()-function obey an ordering.

A basic 'world cycle' could then be characterized as follows:

1. Set the world clock to zero .
2. Assume a world with some properties as objects and certain genotypes at certain positions.
3. [START] Transfer the genotypes into phenotypes while keeping the positions by applying the function for every available genotype. Increase the world clock by some duration .
4. Realize for every phenotype an intake of objects if the object is within the 'intake area' of a phenotype. Increase the world clock by some duration .
5. Realize for every phenotype a consumption of energy . Increase the world clock by some duration .
6. Realize for every phenotype a movement . Increase the world clock by some duration .
7. Realize for every phenotype a creation of offspring . Increase the world clock by some duration .
8. Realize for every phenotype a death-function or . Increase the world clock by some duration .
9. Apply some change to the world with .
10. Calculate the offspring for every Phenotype.
11. Repeat all actions beginning with START

Gerd Doeben-Henisch 2013-01-14