Treffer: Total Memory Optimiser: Proof of concept and compromises

For most usual optimisation problems, the Nearer is Better assumption is true (in probability), This property is taken into account by the classical iterative algorithms, either explicitly or implicitly, by forgetting some information collected during the process, assuming it is not useful any more. However, when the property is not globally true, i.e. for deceptive problems, it may be necessary to keep all the sampled points and their values, and to exploit this increasing amount of information. Such a basic Total Memory Optimiser is presented. We show on an example that it can outperform classical methods on deceptive problems. As it is very computing time consuming as soon as the dimension of the problem increases, a few compromises are suggested to speed it up.