First, it intensifies elitist selection because the fitness variance of dependent program elements exacerbates the difference between the fittest program and mean fitness of the population. Second, the same fitness variance creates a demand for more extensive sampling to recognize a viable component of the solution. The results have made it clear that dependency and fitness structure interact significantly. A required element of pursuing explanations through illustrative problems experimentation is to understand how the hand-designed problems and real problems can be compared. One possible tact on my agenda is to investigate whether software engineering concepts such as dependency flow analysis or decomposability can be associated with the designed problems.
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Innovative Application of GP My second research direction explores the use of GP in the domain of architecture. I was approached by an architect who was seeking a computational technique to help him integrate the natural and artificial processes at a design site into architectural forms. GP is an effective way of exploring conceptual spaces for architects because inheritance allows them to refine while blind variation stimulates them with unpredictable options. The evolutionary metaphor allows a non-specialist to comprehend the algorithmic process. Evolution manipulates the programs that generate the coordinates of the surfaces.