Fodder composition optimization using modified genetic algorithm

Abstract

Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.

The saturation of population fitness as a stopping criterion in genetic algorithm

“…These candidate solutions are encoded into chromosomes [7] and brought into the evolution that is constructed by genetics operators. Each of the chromosomes is assigned with a fitness function [20] that serves as a fitness index. The chromosomes from the same generation would have to compete with one another.…”

“…This act is to ensure that only those with dominant traits of optimisation would have higher possibilities to be selected for passing the genes [21]. Through the execution of genetic operators on these selected chromosomes, a new population / generation of chromosomes is formed [20]. The evolution/searching process will continue until a stopping criterion / threshold is met and the fittest in the last generation will be identified as an optimum solution [23].…”

“…Meta-heuristic algorithms are always used for solving combinatorial problems or hard optimization problems [17] since they can provide good solutions at reasonable computational cost [18,19]. However, they may not be able to guarantee the optimality of solution due to their stochastic nature [20].…”

“…A premature convergence should be avoided as it may led to the acquisition of a local optimum solution instead of global optimum solution [20]. The choice of the stopping criterion would determine if the global optimum solution could be found before its searching process is terminated.…”

Penyusunan Jadwal Asisten Praktikum Menggunakan Algoritma Genetika

“…Algoritma genetika dapat bekerja dengan baik pada berbagai jenis optimasi, baik fungsi analitis maupun data numerik. Selain itu, algoritma genetika sederhanadan mampu memberikan solusi yang baik untuk beberapa permasalahan yang kompleks [11]. Oleh karena itu, penyusunan jadwal asisten praktikum pada penelitian ini akan diselesaikan menggunakan algoritma genetika.…”