Optimasi Asupan GGL Ideal Pada Usia Produktif Dengan Algoritma Genetika - Anita Sindar RM Sinaga (IJAI (Indonesian Journal of Applied Informatics), 2020)
Financial security encourages fast food eating habits, the characteristics of problems that require solving genetic algorithms that have multi-objective and multi-criteria. Based on the mathematical model built, an analysis is performed to find the best (optimal) solution. Optimization is an effort or activity to get the best results with the requirements given. Genetic Algorithm as a branch of Evolution Algorithm is an adaptive method commonly used to solve a value search in an optimization problem. To check the results of the optimization we need a fitness function, which signifies a coded description of the solution. During the process, the parent must be used for reproduction, crossing and mutation to obtain new offspring. Determination of the composition of the ideal GGL for productive age must meet the minimum limits for each component of nutrition. The higher the Fitness value the better the chromosomes become a candidate solution. Offspring results generated from the results of the reproduction process are crossever and mutation. The selection process is carried out to obtain the best chromosomes that will be made into the next generation's population. The best chromosomes  offSpring 10 Fitness 12737.34.