This research introduces Evolutionary Ensemble Search (EES), a system that constructs machine-learning procedures through expert-guided program evolution. EES uses a council to guide the evolution process, allocating tasks to execution specialists and an evolutionary engine that selects and mutates code. The system adapts through session memory and problem-indexed lessons, and achieves a high success rate on various tasks. This contributes a concrete architecture for cumulative executable search and a versioned account of its cross-modal development outcomes.