Borderless Optimization Method Genetic Algorithm
Mustafa Kaya · IntechOpen eBooks · 2023
Genetic algorithms, a stochastic research method, are Darwin’s “survival of the best.” Development of biological systems based on the principle of “survival of the fittest” emerged with the adaptation of the process to the computer environment. In genetic algorithms, processes executed are stored in computer memory similar to natural populations performed on units. Today, there are many linear and nonlinear solutions for optimization problems. The method has been developed. Designing optimization problems with these method variables is assumed to be continuous. But this acceptance does not always valid. In some problems, there are too many design variables and constraints for traditional optimization in solving such problems. The use of this method sometimes gives erroneous results or the solution time is too long. Since genetic algorithms are an intuitive method, a given may not find the optimum result for the problem. However, it optimizes the problems that cannot be solved by known methods or the solution time of the problem is too long. Initial nonlinear optimization genetic algorithms applied to the problems of traveling salesman, workshop scheduling, and syllabus preparation, such as complex optimization problems, have been successfully implemented.