1. A computerized method for projecting information data from a multidimensional space onto a space having fewer dimensions, comprising: receiving a database of N-dimensional data in the form of records having a certain number of variables; defining a metric function for calculating a distance between each record and all the other records in the database; calculating a matrix of distances between each record and all the other records in the database using said metric function; defining a N−1 dimensional space in which each record is defined by N−1 coordinates; calculating the N−1 coordinates of each record in the N−1 dimensional space using an evolutionary algorithm applied to all the records in the database at the same time; defining a best projection of the records onto the N−1 dimensional space as a projection in which a distance matrix between each record and all the other records in the N−1 dimensional space best fits or has minimum differences with the distance matrix of the records calculated in the N-dimensional space, and outputting said best projection of the records to a user, wherein said receiving, defining, calculating and outputting are performed by a data processor, and wherein said evolutionary algorithm comprises combinatory criteria that include marriages, causing two parent records to combine their sets of data, and mutations, causing erroneous self-replications of records, wherein at least a number of marriages and of mutations of individuals are adaptive self-definable internal variables, wherein a hidden point is defined which corresponds to a hidden record or to a hidden variable whose existence is only guessed at, said hidden point is added to the parent population by giving it position coordinates X hi and Y hi in the projection, and wherein the calculation of the evolutionary algorithm is carried out in parallel with and without the hidden point and wherein best fit projections obtained from said two parallel calculations are compared.