Editing Gene expression programming (section)

==Other levels of complexity==
The head/tail domain of GEP genes (both normal and homeotic) is the basic building block of all GEP algorithms. However, gene expression programming also explores other chromosomal organizations that are more complex than the head/tail structure. Essentially these complex structures consist of functional units or genes with a basic head/tail domain plus one or more extra domains. These extra domains usually encode random numerical constants that the algorithm relentlessly fine-tunes in order to find a good solution. For instance, these numerical constants may be the weights or factors in a function approximation problem (see the [[gene expression programming#The GEP-RNC algorithm|GEP-RNC algorithm]] below); they may be the weights and thresholds of a neural network (see the [[gene expression programming#Neural networks|GEP-NN algorithm]] below); the numerical constants needed for the design of decision trees (see the [[gene expression programming#Decision trees|GEP-DT algorithm]] below); the weights needed for polynomial induction; or the random numerical constants used to discover the parameter values in a parameter optimization task.