An Introduction to Genetic Algorithms: A survey A practical Issues
Mohamed Abdellatif Hussein, Abd Allah A. Mousa · 2014
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 Finan- cial Engineering. Because of their population approach, they have also been extended to solve other search and optimization problems efficiently, in- cluding multimodal, multiobjective. In this paper, a brief description of a simple GA, GAs vs. traditional methods and GAs to handle constrained optimiza- tion problems are described. Also, GAs for multiobjective optimization MOP is proposed. Thereafter, GAs applications are presented. The intended audi- ence 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. Index Terms— Genetic algorithms; constrained Optimization; Multiobjective optimization.