Compare and contrast the effectiveness of heuristic and non-heuristic algorithms for optimizing solutions.

Award [6 max].
A heuristic is an algorithm that does not guarantee an optimal solution;
Instead, a stopping criterion is determined before running;
Heuristic sacrifices accuracy (optimization) for speed;
Heuristics use randomness to escape local extrema / for exploration;
Unlike heuristics, brute-force does not build off previous success/is entirely explorative and novel;
Given enough processing power/time, a brute force approach will always produce an optimal solution;
A brute-force approach is not suitable / heuristic only suitable approach for non-polynomial (computationally intractable) problems;
The number of different permutations if the number of cities is high will make the problem difficult to solve computationally;
In some situations, an optimal solution may be necessary ethically (e.g. where people’s;
lives are at risk) / In other situations, a non-optimal solution can be used; 

This question asked candidates to compare and contrast the effectiveness of heuristic and non-heuristic algorithms for optimizing solutions. Most candidates gained between two and 4 of the available 6 marks. The main points mentioned were that heuristic compared to non-heuristics algorithms tend to produce near-optimal solutions rather than optimal ones, non-heuristics are not suitable for computationally intractable problems, and a heuristic sacrifices accuracy (optimization) for speed. Better answers explained that heuristic algorithms use previous success to inform their search whereas brute-force algorithms are entirely explorative and novel.