Genetic algorithms belong to the larger class of evolutionary algorithms (EA). Genetic algorithm generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In this paper various selection methods has been described ...
A Fuzzy Expert System for Damage Assessment Using Genetic Algorithms and Neural Networks An efficient knowledge-acquisition support method is required for improvement and maintenance of the knowledge base in durability evaluation of an RC bridge deck. Such a method is proposed in this paper to automat...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Ba...
Some of the problems that arise in the methods based on hand segmentation are their sensitivity to illumination changes, a need for initialization step, the problems of the sensors (like Kinect) to work well in outdoor locations and a need for controlling the scenario to exhibit good performance...
A new selection method, entropy-Boltzmann selection, for genetic algorithms (GAs) is proposed. This selection method is based on entropy and importance sampling methods in Monte Carlo simulation. It naturally leads to adaptive fitness in which the fitness function does not stay fixed but varies wit...
This paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM), one of the genetic algorithms (GAs) used for feature selec
Recently, several types of attribute selection methods have been proposed that use different approaches to obtain representative subsets of the attributes. However, population-based evolutionary algorithms like Genetic Algorithms (GAs) have been proposed to provide remedies for these drawbacks by avoiding ...
Furthermore, unlike so many other search algorithms, GAs conducts a global search rather than a local, or greedy search. The basic concept is to evolve a population of individuals, each of which is a possible solution to a specific issue. A genetic algorithm is made up of three main ...
algorithms, and validation methods have been used to evaluate new and existing techniques. By following the SLR protocol, we allow the replication of our revision process and minimize the chances of bias while classifying the included studies. By mapping issues and experiment settings, our SLR ...
Methods of this type have low computational complexity, run quickly, and are convenient for selecting satellites through mathematical analysis, equivalent substitution, and weighting methods based on matrix theory. However, most current satellite selection algorithms are verified with only simulation data, ...