## Metaheuristics and machine-learning

Par nojhan le mardi 19 décembre 2006, 00:00 - Divers - Lien permanent

Metaheuristics and machine-learning algorithms shares a large number of characteristics, like stochastic processes, manipulaton of probability density functions, etc.

One of the interesting evolution of the research on metaheuristics these years is the increasing bridge-building with machine-learning. I see at least two interesting pathways: the use of metaheuristics in machine-learning and the use of machine-learning in metaheuristics.

The first point is not really new, machine-learning heavily use
optimization, and it was natural to try stochastic algorithms where local
search or exact algorithms failed. Nevertheless, there is now a sufficient
litterature to organize some special sessions in some symposium. For 2007,
there will be a *special session on Genetics-Based Machine Learning* at
CEC, and a *track
on Genetics-Based Machine Learning and Learning Classifier Systems* at
GECCO. These events are centered around "genetic" algortihm (see the posts on
the IlliGAL blog : 1, 2), despite the fact that there are several papers using
other metaheuritics, like simulated annealing, but this is a common drawback,
and does not affect the interest of the subject.

The second point is less exploited, but I find it of great interest. A simple example of what can be done with machine-learning inside metaheuristic can be shown with estimation of distribution algorithms. In these metaheuristics, a probability density function is used to explicitely build a new sample of the objective function (a "population", in the evolutionary computation terminology) at each iteration. It is then crucial to build a probability density function that is related to the structure of the objective function (the "fitness landscape"). There, it should be really interesting to build the model of the pdf itself from a selected sample, using a machine-learning algorithm. There is some interesting papers talking about that.

If you mix these approaches with the problem of estimating a Boltzmann distribution (the basis of simulated annealing), you should have an awesome research field...