Evolutionary computation and AI engineering

Given a black-box system and a set of inputs, if one can express the system's functional goal(s) based on a measure of its outputs, i.e. Goals(Measure(Outputs(Inputs))), then evolutionary computation can grow the system's structure towards a mechanism capable of reaching the desired goal(s) (exactly to what extent evolution can build such a system, and under what conditions, is still a hot research topic). In other words, evolutionary computation seems to act as a universal behavioral to structural specification converter.

What does this mean to AI engineering?

  • first, at a strictly engineering level, using the evolutionary computation principles would allow designing an AI system by mixing structural and behavioral specifications. In this way one would eliminate the need for evolution to find a structure for those building blocks for which a structural specification has been designed, while resorting to evolutionary algorithms only in those cases when finding a structure is elusive.
  • second, pursuing a mixed structural-behavioral modeling essentially means that no more than a helping hand is given to evolution by hinting certain directions at various evolution stages of a complex system: designing a structural specification of a building block (or even of a top-level system) only helps evolution to skip the stage of finding a structure for that particular building block, but it does not help evolution any further in finding (i.e. "designing") a structure for the component sub-blocks. At one end, specifying the structure of every single building block in a system means completely eliminating the need for evolution to synthesize any structure at all, while at the other end, if no structural hints are given at all, it will be left up to evolution to find the entire inner structure of the system. In other words, a mixed structural-behavioral modeling only acts as a human-operated evolution accelerator, while being consistent with the evolutionary paradigm.

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