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 GEP-RNC algorithm below); they may be the weights and thresholds of a neural network (see the GEP-NN algorithm below); the numerical constants needed for the design of decision trees (see the 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.
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