1. A computer medium of sound or image recognition comprising: one or more sensors or receivers responsive to signals; a computer operatively coupled to the one or more sensors, the computer comprising a central processing unit; one or more memories, at least one of the one or more memories storing a software program comprising the steps of: defining a plurality of distributions of known database records onto respective training and testing subsets; training and testing a first generation set of prediction algorithms using the plurality of distributions of the database records, each of said prediction algorithms being associated with a first different distribution of said database records; assigning a fitness score to each of the prediction algorithms; feeding the set of prediction algorithms to an evolutionary algorithm which generates a set of one or more second generation prediction algorithms and assigns a fitness score to each; continuing to feed each generational set of prediction algorithms to the evolutionary algorithm until a termination event occurs, wherein said termination event is at least one of; a prediction algorithm generated with a fitness score equal to or exceeding a defined minimum value, the maximum fitness score of successive generational sets of prediction algorithms converging to a given value, or a certain number of generations having been generated; selecting a prediction algorithm having a best fitness score; and using the distribution of database records associated with said selected prediction algorithm in performing supervised learning, said supervised learning including training and testing of prediction algorithms to obtain a trained prediction algorithm; generating a population of prediction algorithms, wherein each of said prediction algorithms is trained and tested according to a second different distribution of the records of the data set in the complete database onto a training data set and a testing data set, each second different distribution being created as one of a random or pseudorandom distribution, each prediction algorithm of said population being trained according to its own distribution of records of the training set and being validated in a blind way according its own distribution on the testing set, and a score reached by each prediction algorithm being calculated in the testing phase representing its fitness; providing an evolutionary algorithm which combines the different models of distribution of the records of the complete data set in a training and in a testing set, which sets are represented each one by a corresponding prediction algorithm trained and tested on the basis of said training and testing data set according to the fitness score calculated in the previous step for the corresponding prediction algorithm, the fitness score of each prediction algorithm corresponding to one of the different distributions of the complete data set on the training and the testing data sets being the probability of evolution of each prediction algorithm or of each said distribution of the complete data set on the training and testing data sets; repeating the evolution of the prediction algorithm generation for a finite number of generations or till the output of the genetic algorithm converges to a best solution and/or till the fitness value of at least some prediction algorithm related to an associated data records distribution has reached a desired value; and setting the data records distribution for the best solution as the optimized training and testing subsets for training and testing prediction algorithm; and an output system providing an indication of the signals detected by the one or more sensors.