Genetic algorithms can be used in order to solve optimization problems. Such a technique may be used in order to solve differential equations.

Considering the epistemic and ontological sense of principles and functions and the way they contribute to the creation and evolution of institutional structures a model was developed in which the reaction-diffusion of mimes (Dawkins) in a human niche (Popper) is described as a Brusselator that...

The paper aims to model the underground economy using two different models: one based on the labor supply method and a generalized model for the allocation of time. The model based on the labor supply method is conceived as a simulating one in order to determine some reasonable thresholds of the...

We simulate possible growth paths assuming that the Romanian economy behaves according to the hypothesis of the Uzawa-Lucas model. By calibrating the model to the Romanian economy, we are able to forecast the evolution of the Romanian GDP and the proportion of human capital which will be used in...

Evolutionary algorithms represent a powerful tool for a wide set of problems, based on biological principles. The main goal of this article is to underline the principle of genetic acquisitions. To do this, we will use mutations with respect to the gradient of an objective function.

The paper analyzes the regression problem for small and undersized sample. Two classical algorithms are compared: Simulated Annealing (SA) versus Repetitive Stochastic Guesstimation (RSG). An improved version of RSG is built and compared to the previous two algorithms. The author concludes that...

The paper presents an econometric estimation for the Aghion and Blanchard (1994) model of the speed of transition for the case of Romania. All the econometric estimations were done by means of three stochastic optimization algorithms (RGS, RSGBOOT and SA), after preliminary Monte Carlo...

The iterative deterministic optimization method could not more find multiple global minima of a given objective function ( [6] ). Generally, the probabilistic optimization algorithms have not this restrictive behaviour, to determine only a single global minimum point. In this context we’ll...

A genetic algorithm is convergent when genetic mutations occur on the objective function gradient direction. These genetic mutations are called genetic acquisitions (Mateescu, 2005). We improved the algorithm and its implementation by using the characteristics of parents in order to generate new...

The model is one theoretical approach within a broader research program that could verify the nonlinear conjectures made to quantify and predict potential discontinuous behaviour. In this case, the crisis behaviour associated with financial funds reallocation among various credit instruments,...