Despite the title of this blog, the term *metaheuristic* is not
really well defined.

One of the first occurence of the term can (of course) be found in a paper
by Fred Glover^{[1]}: *Future Paths for Integer Programming and
Links to Artificial Intelligence*^{[2]}. In the section
concerning *tabu search*, he talks about *meta-heuristic*:

Tabu search may be viewed as a "meta-heuristic" superimposed on another heuristic. The approach undertakes to transcend local optimality by a strategy of forbidding (or, more broadly, penalizing) certain moves.

In the AI field, a *heuristic* is a specific method that help solving
a problem (from the greek for *to find*), but how must we understand the
*meta* word ? Well, in greek, it means "after", "beyond" (like in
*metaphysic*) or "about" (like in *metadata*). Reading Glover,
*metaheuristics* seems to be *heuristics beyond heuristics*,
which seems to be a good old definition, but what is the definition nowadays ?
The litterature is really prolific on this subject, and the definitions are
numerous.

There is at least three tendencies :

- one that consider that the most important part of
*metaheuristcs*is the gathering of several heuristics, - one other that promotes the fact that
*meta*heuristics are designed as generalistic methods, that can tackle several problems without major changes in their design, - the last one that use the term only for evolutionnary algorithms when they
are hybridicized with local searches (methods that are called
*memetic algorithms*in the other points of vue).

The last one is quite minor in the generalistic litterature, it can mainly be found in the field of evolutionnary computation, separate out the two other tendencies is more difficult.

Here are some definitions gathered in more or less generalistic papers:

"iterative generation process which guides a subordinate heuristic by combining intelligently different concepts for exploring and exploiting the search space" (Osman and Laporte, 1996

^{[3]})

"(metaheuristics) combine basic heuristic methods in higher level frameworks aimed at efficiently and effectively exploring a search space" (Blum and Roli, 2003

^{[4]})

"a metaheuristic can be seen as a general-purpose heuristic method designed to guide an underlying problem-specific heuristic (...) A metaheuristic is therefore a general algorithmic framework which can be applied to different optimization problems with relative few modifications to make them adapted to a specific problem." (Dorigo and Stützle, 2004

^{[5]})

"(metaheuristics) apply to all kinds of problems (...) are, at least to some extent,

stochastic(...) direct, i.e. they do not resort to the calculation of the gradients of the objective function (...) inspired byanalogies: with physics, biology or ethology" (Dréo, Siarry, Petrowski and Taillard, 2006^{[6]})

One can summarize by enumerating the expected characteristics:

- optimization algorithms,
- with an iterative design,
- combining low level heuristics,
- aiming to tackle a large scale of "hard" problems.

As it is pointed out by the last reference, a large majority of
metaheuristics (well, not to say *all*) use at least one stochastic
(probabilistic) process and does not use more information than the solution and
the associated value(s) of the objective function.

Talking about *combining* heuristics seems to be appropriate for
*Ant Colony Optimization*, that specifically needs one (following
Dorigo's point of vue), it can be less obvious for *Evolutionnary
Algorithms*. One can consider that *mutation*, or even the method's
strategy itself, is a heuristic, but isn't it too generalistic to be called a
*heuristic* ?

If we forget the difficulty to demarcate what can be called a
*heuristic* and what is the scope of the term *meta*, one can
simply look at the use of the term among specialists. Despite the fact that the
definition can be used in several fields (data mining, machine learning, etc.),
the term is used for optimization algorithms. This is perhaps the best reason
among others: the term permits to separate a research field from others, thus
adding a little bit of marketing...

I would thus use this definition:

Metaheuristics are algorithms designed to tackle "hard" optimization problems, with the help of iterative stochastic processes. These methods are manipulating direct samples of the objective function, and can be applied to several problems without major changes in their design.

#### Notes

[1] A recurrent joke says that whatever is your new idea, it has already be written down by Glover

[2] Comput. & Ops. Res.Vol. 13, No.5, pp. 533-549, 1986

[3] *Metaheuristic: A bibliography*, Annals of
Operations Research, vol. 63, pp. 513-623, 1996

[4] *Metaheuristics in combinatorial optimization:
Overview and conceptual comparison*, ACM Computing Surveys, vol. 35, issue
3, 2003

[5] *Ant Colony Optimization*, MIT Press, 2004

[6] *Metaheuristics for Hard Optimization*,
Springer, 2006