Document Type : Original Article
Authors
1
Department of Basic Engineering Sciences, Faculty of Engineering, Menoufia University, Shibin El-Kom, Egypt.
2
Department of Basic Engineering Sciences, Faculty of Engineering, Menoufia University, Shibin El-Kom, Egypt.+
3
Department of Mathematics, Faculty of Science, Qassim University, Saudi Arabia.
4
Department of Mathematics, Faculty of sciences, Taif University, Saudi Arabia.
Abstract
Abstract:
The Genetic Algorithm (GA) is a relatively simple heuristic algorithm that can be implemented in a straightforward manner. It can be applied to a wide variety of problems including unconstrained and constrained optimization problems, nonlinear programming, stochastic programming, and combinatorial optimization problems. It is widely used in several fields such as management decision making, data processing ...Information and Financial Engineering. Because of their population approach, they have also been extended to solve other search and optimization problems efficiently, including multimodal, multiobjective. In this paper, a brief description of a simple GA, GAs vs. traditional methods and GAs to handle constrained optimization problems are described. Also, GAs for multiobjective optimization MOP is proposed. Thereafter, GAs applications are presented. The intended audience of this paper is those who wish to know the main concepts of GAs and how to apply it to different optimization problems. Also, to familiarize readers to the algorithm proceeding.
Keywords
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