## Error metrics

Par nojhan le jeudi 5 juillet 2007, 00:00 - Divers - Lien permanent

Many metrics are used to assess the quality of approximation found by
metaheuristics. Two of them are used really often: distance to the true optimum
according to its *position* and to its *value*.

Unfortunately, the objective function's shape can vary a lot in real-world problem, making these metrics difficult to interpret. For example, if the optimum is in a very deep valley (in value), a solution close to it in position may not signifiate that the algorithm have well learn the shape of it. Inversely, a solution close to an optimum in value may not signifiate that it is in the same valley.

One metric that can counter thse drawbacks is a distance taking into account the parameters of the problem as well as the value dimension.

Anyway, the question of the type of distance to use is dependent of the problem.