Year: 2022
Type: article
Abstract: Genetic algorithms (GAs) are a class of optimization techniques inspired by the process of natural selection and evolution. These algorithms are widely used to solve complex problems where traditional optimization methods fall short due to the high-dimensional search spaces or the non-linear nature of the objective function. GAs iteratively evolve a population of candidate solutions through selection, crossover, and mutation operations to find optimal or near-optimal solutions. This paper provides an overview of genetic algorithms, their key components, and their applications across various domains, including engineering, finance, and machine learning. Furthermore, we explore the strengths and limitations of GAs in solving real-world optimization problems, comparing them with other optimization methods. By highlighting the potential of GAs in handling multi-objective and dynamic optimization problems, this paper concludes by discussing the future prospects and advancements in genetic algorithm research.
Source: Computer Science Bulletin
DOI: 10.71465/csb90
Author: Dr. Waqas Anwar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7143031108