The Multiple Knapsack Problem (MKP) is a combinatorial optimization challenge where items with given prizes and multi-dimensional weights must be assigned to multiple knapsacks with capacity constraints, maximizing total prize without exceeding any knapsack's capacity. 
We use Ant Colony Optimization (ACO) to solve this problem, where artificial ants probabilistically construct solutions guided by pheromone trails and heuristic information. 
Each ant builds a solution by sequentially selecting items based on a probability distribution that combines pheromone intensity and heuristic desirability. 
Your task is to evolve a `heuristics` function that generates an n-dimensional heuristic vector to guide ant movement, where n is the number of items. 
The heuristic vector should capture item desirability to help ants make better selection decisions, with the goal of maximizing total prize across all instances (called "score" or "objective" of solution).