The Bin Packing Problem (BPP) is a combinatorial optimization challenge where items of varying sizes must be packed into bins of fixed capacity, minimizing the number of bins used while respecting capacity constraints. 
In the offline version, all item sizes are known in advance, allowing for more sophisticated packing strategies. 
We use Ant Colony Optimization (ACO) to solve this problem, where artificial ants probabilistically construct packing solutions guided by pheromone trails and heuristic information.
Each ant builds a solution by sequentially assigning items to bins based on a probability distribution that combines pheromone intensity and heuristic desirability. 
Your task is to evolve a `heuristics` function that generates an n×n heuristic matrix to guide ant movement, where n is the number of items. 
The goal of minimizing the average number of bins used across all instances (called "score" or "objective" of solution).