genetic algorithm

PERFORMANCE ANALYSIS OF CNN-ENHANCED GENETIC ALGORITHM FOR TOPOLOGICAL OPTIMIZATION IN METAMATERIAL DESIGN

The Combination of Convolutional Neural Networks (CNN) and Genetic Algorithms (GA) provides a promising approach for topological optimization of complex lattice structures. Lattice structures are commonly used as base in the design of high-performance metamaterials. This paper presents a review of the effectiveness and efficiency of the CNN-GA method. We will examine the ability of the method to generate optimal complex structures while minimizing material usage. CNN is utilized mainly as an analysis instrument.

COMBINED APPROACH TO BUILDING OPTIMAL ROUTES FOR INDIVIDUAL TRIPS IN A MOBILE APPLICATION

The paper deals with building optimal routes for individual trips under the influence of many factors and possible changes in the input parameters (such as weather conditions, traffic congestion, etc). We have analyzed four classes of algorithms for solving the traveling salesperson problem and evaluated their applicability in a tourist mobile application. The software should be a mobile application since only a few travelers take computers or laptops but most of them carry smartphones.

SEARCH FOR A DATA TRANSMISSION ROUTE IN A WIRELESS SENSOR NETWORK USING A GENETIC ALGORITHM

The article is devoted to the application of a genetic algorithm for determining the optimal route in a wireless sensor network. The paper presents a classification of data routing strategies based on: the method of determining routes, network structure, network operations, and communication organiser. The genetic algorithm is classified as a multi-path routing strategy, since its use allows obtaining a set of routes.

COMPUTATIONAL COMPLEXITY EVALUATION OF A GENETIC ALGORITHM

The article is devoted to the estimation of computational complexity of a genetic algorithm as one of the key tools for solving optimisation problems. The theoretical aspects of computational complexity of algorithms and the interrelation of elements of a genetic algorithm are considered. The main types of computational complexity of algorithms are described: time, simple and asymptotic. Five basic rules for calculating the asymptotic complexity are given.

Використання генетичних алгоритмів для апроксимації функцій дійсними поліномами

Наведено метод апроксимації функцій поліномами з дійсними степенями, в якому підбір степеня здійснюється за допомогою генетичного алгоритму.

The method of approximation of functions by polynomials with real powers, which is the power of selection with a genetic algorithm.

A metaheuristic approach to improve consistency of the pairwise matrix in AHP

In this paper, we are interested in modifying inconsistent pairwise comparison matrix which is a critical step in the AHP methodology, where decision makers have to improve the consistency by revising the process.  To this end, we propose an improved genetic algorithm (GA) to allow decision makers to find an appropriate matrix and adjust the consistency of their judgment without loss of original comparison matrix.  Numerical results with different dimensions of matrices taken randomly show the effectiveness of these strategy to improve and identify the consistency of pa

GENETIC ALGORITHM AS A TOOL FOR SOLVING OPTIMISATION PROBLEMS

The article focuses on the peculiarities of using the genetic algorithm (GA) for solving optimization problems. It provides a classification of optimization problems and offers a detailed description of the structural elements of the GA and their role in solving the traveling salesman problem. To assess the impact of GA parameters on its effectiveness, a study on the influence of population size on the length of the traveling salesman's route is conducted.

Genetic algorithm parenting fitness

The evolution scheme phase, in which the genetic algorithms select individuals that will form the new population, had an important impact on these algorithms.  Many approaches exist in the literature.  However, these approaches consider only the value of the fitness function to differenciate best solutions from the worst ones.  This article introduces the parenting fitness, a novel parameter, that defines the capacity of an individual to produce fittest offsprings.  Combining the standard fitness function and the parenting fitness helps the genetic algorithm to be more efficient, hence, pro

Hybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet

In this paper, we solve the Moroccan daily diet problem based on 6 optimization programming $(P)$ taking into account dietary guidelines of US department of health, human services, and department of agriculture.  The objective function controls the fuzzy glycemic load, the favorable nutrients gap, and unfavorable nutrient excess.  To transform the proposed program into a line equation, we use the integral fuzzy ranking function.  To solve the obtained model, we use the Hybrid Firefly Genetic Algorithm (HFGA) that combines some advantages of the Firefly Algorithm (FA) an

Optimal fuzzy deep daily nutrients requirements representation: Application to optimal Morocco diet problem

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