  The Santa Fe Ant model problem has been extensively used to investigate, test
and evaluate Evolutionary Computing systems and methods over the past two
decades. There is however no literature on its program structures that are
systematically used for fitness improvement, the geometries of those structures
and their dynamics during optimization. This paper analyzes the Santa Fe Ant
Problem using a new phenotypic schema and landscape analysis based on executed
instruction sequences. For the first time we detail systematic structural
features that give high fitness and the evolutionary dynamics of such
structures. The new schema avoids variances due to introns. We develop a
phenotypic variation method that tests the new understanding of the landscape.
We also develop a modified function set that tests newly identified
synchronization constraints. We obtain favorable computational efforts compared
to those in the literature, on testing the new variation and function set on
both the Santa Fe Trail, and the more computationally demanding Los Altos
Trail. Our findings suggest that for the Santa Fe Ant problem, a perspective of
program assembly from repetition of highly fit responses to trail conditions
leads to better analysis and performance.
