The “ZIP” adaptive automated trading algorithm has been demonstrated to outperform human traders in experimental studies of continuous double auction (CDA) markets populated by mix- tures of human and “software robot” traders. Previous papers have shown that values of the eight parameters governing behavior of ZIP traders can be automatically optimized using a genetic al- gorithm (GA), and that markets populated by GA-optimized traders perform better than those populated by ZIP traders with manually-set parameter values. This paper introduces a more so- phisticated version of the ZIP algorithm, called “ZIP60”, which requires the values of 60 pa- rameters to be set correctly. ZIP60 is shown here to produce significantly better results in com- parison to the original ZIP algorithm (called “ZIP8” hereafter) when a GA is used to search the 60-dimensional parameter space. It is also demonstrated here that this works best when the GA itself has control over the dimensionality of the search-space, allowing evolution to guide the ex- pansion of the search-space up from 8 parameters to 60 via intermediate steps. Principal compo- nent analysis of the best evolved ZIP60 parameter-sets establishes that no ZIP8 solutions are em- bedded in the 60-dimensional space. Moreover, some of the results and analysis presented here cast doubt on previously-published ZIP8 results concerning the evolution of new ‘hybrid’ auction mechanisms that appeared to be improvements on the CDA: it now seems likely that those results were actually consequences of the relative lack of sophistication in the original ZIP8 algorithm, because “hybrid” mechanisms occur much less frequently when ZIP60s are used.