Application of a Method Based on Computational Intelligence for the Optimization of Resources Determined from Multivariate Phenomena

Abstract

The optimization of complex systems one of whose variables is time has been attempted in the past but its inherent mathematical complexity makes it hard to tackle with standard methods. In this paper we solve this problem by appealing to two tools of computational intelligence: a) Genetic algorithms (GA) and b) Artificial Neural Networks (NN). We assume that there is a set of data whose intrinsic information is enough to reflect the behavior of the system. We solved the problem by, first, designing a system capable of predicting selected variables from a multivariate environment. For each one of the variables we trained a NN such that the variable at time t+k is expressed as a non-linear combination of a subset of the variables at time t. Having found the forecasted variables we proceeded to optimize their combination such that its cost function is minimized. In our case, the function to minimize expresses the cost of operation of an economic system related to the physical distribution of coins and bills. The cost of transporting, insuring, storing, distributing, etc. such currency is large enough to guarantee the time invested in this study. We discuss the methods, the algorithms used and the results obtained in experiments as of today.