Optimization of Genetic Algorithms for Non-Linear Programming Problem Solving

Abstract

This research aims to optimize the use of genetic algorithms in solving linear and non-linear programming problems. Genetic algorithms, inspired by the evolutionary process of nature, have the ability to find optimal solutions to a wide range of complex optimization problems. The method used in this study is a qualitative method with a literature study and library research approach. This study analyzes a variety of relevant literature to understand the basic principles of genetic algorithms, as well as their application in linear and non-linear programming. The results of the literature study show that genetic algorithms have advantages in dealing with optimization problems that have many variables and complex limitations. Additionally, the algorithm is able to avoid pitfalls on local solutions and has flexibility in its application to various fields, including engineering, economics, and biology. The study also discusses several case studies that show the success of genetic algorithms in solving linear and non-linear programming problems, and compares their performance with traditional optimization methods. In conclusion, genetic algorithms are an effective and efficient tool in solving linear and non-linear programming problems. An in-depth literature study shows that this algorithm has great potential to be applied to various optimization problems in various fields. This research makes a significant contribution to the development of better and more efficient optimization methods in the future.