We present a new method based on genetic algorithms which permits to determine efficiently the partition function and the excitation spectrum of few-body quantum systems. In our approach, we use a variational formulation for the partition function Z of the system as a functional of its...

This paper proposes a model selection methodology for feedforward network models based on the genetic algorithms and makes a number of distinct but inter-related contributions to the model selection literature for the feedforward networks. First, we construct a genetic algorithm which can search...

We present a new method based on evolutionary algorithms which permits to determine efficiently the ground state of the time-independent Schrödinger equation for arbitrary external potentials. The approach relies on the variational principle. The ground-state wave function of a given...

Intercommunity disease spread can be modeled using a collection of discrete community “patches” with continuous population flow between them. In a susceptible–infected–susceptible (SIS) model residents of a community may either be classified as susceptible or infected. Infected...

In this study, we present empirical analysis of statistical properties of mating networks in genetic algorithms (GAs). Under the framework of GAs, we study a class of interaction network model—information flux network (IFN), which describes the information flow among generations during...

We demonstrate the power of genetic algorithms to construct a cellular automata model simulating the growth of 2D close-to-circular clusters, revealing the desired properties, such as the growth rate and, at the same time, the fractal behavior of their contours. The possible application of the...