In 'real life' the genotype is the 'code base' for the construction of a phenotype, complex bodies, which are those entities which finally will interact with the environment (earth)(cf. 5.12). In real life there is no need for an explicit fitness function because the fitness function is 'implicitly given' in the way how the environment interacts with the population of the phenotypes.
The transition from genotype to phenotype is usually called growth (or more technically ontogeny as well as ontogenesis), or as formula .
This is a rather complex process, a real masterpiece of nature. To include this in computational evolution as evolutionary programming is even in simple cases highly demanding in computation resources.
We will not discuss the subject of 'growth' here but skip to the subject of phenotypes acting as learning systems in an environment 3.1.
From the biological case we know that a phenotype is not defined by pure chance only but in a combination of a chance driven mechanism of genetic combinatorics combined with selection mechanism by the environment. Thus only those phenotypes can 'survive' which are adapted to the given environment (cf. some general books about this topic Duve (1995)[59], Davies (1998)[56]).
Every real body represents therefore a minimal combination of components which are necessary to survive in an environment like the earth. Although we do not intend to reconstruct real bodies within our learning theory it can be helpful to summarize here some key factors of real living systems to be given for a successful living (Remark: the enumeration below does not describe the most simple systems but those which are already quite able to move around in an environment).
From this we can derive minimal requirements for an environment with a spatial structure providing free energy for intake by an open system , which can move within the environment.