The Capacitated Vehicle Routing Problem (CVRP) is a combinatorial optimization challenge where vehicles with limited capacity must deliver goods from a central depot to multiple customer locations, minimizing total travel distance while respecting vehicle capacity constraints. 
We use the LEHD (Learning with Heavy Decoder) model to solve this problem, which employs a transformer-based neural architecture with a lightweight encoder (1 layer) and a heavy decoder (6 layers) that sequentially selects nodes to construct routes. 
Your task is to evolve a `heuristics` function that generates an attention bias matrix from distance matrices and customer demands, guiding the decoder's attention mechanism to improve route construction, with the goal of minimizing the total distance traveled across all vehicle route (called "score" or "objective" of solution).