genetic algorithm

Solving the optimal diet problem necessarily involves estimating the daily requirements in positive and negative nutrients.  Most approaches proposed in the literature are based on standard nominal estimates, which may cause shortages in some nutrients and overdoses in others.  The approach proposed in this paper consists in personalizing these needs based on an intelligent system.  In the beginning, we present the needs derived from the recommendations of experts in the field of nutrition in trapezoidal numbers.  Based on this model, we generate a vast database.  The latter is used to educ

 Features of designing a system of automated generation of poetic works, which opens up new opportunities for artistic speech and show business, especially the preparation of poems and songs have been considered. Quite often lyrics without special content become successful due to the lack of complex plots, as well as due to the unobtrusiveness and ease of perception by listeners. Well-known literature sources and available software products that can generate poetic works by combining different methods and algorithms are analyzed.

This paper presents a new method to solve a challenging problem and a topic of current research namely the selection of optimal shape parameters for the Radial Basis Function (RBF) collocation methods in  both interpolation and nonlinear Partial Differential Equations (PDEs) problems.  To this intent, a compromise must be made to achieve the conflict between accuracy and stability referred to as the trade-off  or uncertainty principle.  The use of genetic algorithm and path-following continuation allows us on the one hand to avoid the local optimum issue associated with RBF interpolation ma

The main difficulty of the meshless methods is related to the support of shape functions.  These methods become stable when sufficiently large support is used.  Rather larger support size leads to higher calculation costs and greatly degraded quality.  The continuous adjustment of the support size to approximate the shape functions during the simulation can avoid this problem, but the choice of the support size relative to the local density is not a trivial problem.  In the present work, we deal with finding a reasonable size of influence domain by using a genetic algorithm coupled with hig

Entity Resolution  is the task of mapping the records within a database to their corresponding entities.  The entity resolution problem presents a lot of challenges because of the absence of complete information in records, variant distribution of records for different entities and sometimes overlaps between records of different entities.  In this paper, we have proposed an unsupervised method to solve this problem.  The previously mentioned problem is set as a partitioning problem.  Thereafter, an optimization algorithm-based technique is proposed to solve the entity resolution problem.  T

In this work, the MOSFET device is considered.  The carrier densities in the MOSFET are modeled by the drift-diffusion equation.  We manipulate the formulas of the charge density at the equilibrium in order to derive a simple Poisson's or Laplace's equation.  To formulate a shape optimization problem, we have defined a cost functional.  The existence of an optimal solution is proved.  To solve the involved optimization problem, we have designed a numerical approach based on the finite element method combined with the genetic algorithm.  Several numerical examples are established to prove th

A method for constructing the empirical models of complex processes has been developed on the basis of genetic algorithms which, compared to the inductive method of self-organization of models, significantly reduces computer time for their implementation. An approach has been used that allows a complex model to be considered as a composition of three components, i.e. a linear trend, an oscillatory component with non-multiple frequencies and a regression equation which simplifies the process of building complex models.

The problem of optimal processing tasks in the nodes of a distributed information system on the basis of a mathematical model belonging to a class of two-clustering problems, for which an optimization problem with a fractional linear target function is formulated, was investigated.

One of modern possible problem solutions of analysis and synthesis in electromechanical systems is the recourse to a genetic algorithm as a method of artificial intelligence. The originality of the proposed approach lies in the usage of fractional order models to solve the above-mentioned problem. The quality function is proposed to be used in the developed algorithms for analysis and synthesis procedures of electromechanical systems. It is also proposed to get the desired outcome of results deviation from the set values by means of quality function control after each iteration.

Modified routing algorithms are presented based on basic meta-heuristic algorithms: ant colony optimization, genetic and simulated annealing to determine the best route for information flows in self-organized networks. An ant colony optimization is based on the use of the probability parameter for the transition between the nodes located between the source node and the receiving node. To solve the problem of optimization of routing in a simulated annealing, its modification is proposed by adding or removing a transit node based on the coverage of the reaching range of neighboring nodes.