Artificial evolution has become an important topic in the field of artificial life around 1960. Artificial evolution follows two main goals:

(1)Scientists use artificial evolution to understand the principles of biological behaviour by looking at the evolutionary process. They are using the synthetic methodology, which is 'understanding by building'.

(2)Artificial evolution is also used for optimising solutions of a certain problem; it might come up with new solutions, whereas human beings have biased ideas on how to solve this problem. A well-known example is the tubing problem of Ingo Rechenberg.

The three pioneers in artificial evolution were:

* Ingo Rechenberg – Evolutionary Strategies (optimising problems)
* John Holland - Genetic Algorithms (adaptivity of natural systems)
* L.J. Fogel – Genetic Programming (optimising problems)

In the picture below an overview of the process of artificial evolution is shown:
Through the interaction with the environment, the genotype is translated into the phenotype (process of development). Then there is a selection (for details about selection strategies see below) on the phenotype. The selected individuals can reproduce. During this process, mutation and crossover occur on the genotype; then a new generation arises. This process goes on and on. The criterion to select an individual is the fitness function which has to be defined at the outset. Like in most simulations using genetic algroithms with MorphEngine genotype and phenotype are the same. The phenotype is created with MeCreator and is not exposed to any environmental influence after having been created.

The selection of the individuals is always a trade-off: on the one hand one would like to choose just the best ones, but as the environment is changing, these individuals might not be the best one's in the future - they justlead to local maxima. Therefore, usually a selection strategy is chose that selects the best individuals, whereas the other individuals are selected by roulette-wheel-selection, giving individuals with a lower fitness less chance to be selected and vice versa. This trade-off is also known as the exploration-exploitation trade-off which is in short: getting the right mix of individuals.