The dominance-dissimilarity mechanism can be enhanced by incorporating additional metrics that capture the nuances of heuristic performance and diversity. One potential improvement is the integration of a multi-faceted similarity measure that goes beyond code structure to include semantic similarity. This could involve using natural language processing techniques to analyze the descriptions of heuristics, allowing for a more comprehensive understanding of their functional similarities and differences. Additionally, incorporating adaptive weighting for the dominance and dissimilarity scores based on the current state of the population could help prioritize exploration in underrepresented areas of the search space. This adaptive approach would allow the mechanism to dynamically adjust its focus, promoting diversity when the population becomes homogenous and emphasizing convergence when a promising region is identified. Furthermore, leveraging ensemble learning techniques could provide a more robust assessment of heuristic performance by aggregating multiple evaluation criteria, thus refining the selection process for parent heuristics.