In this paper, a new algorithm based on genetic algorithm
is proposed, of which the issue is converted into a multiple attribute optimization problem, then genetic algorithm
is used to solve the global optimization problem.
are a type of optimization algorithms, meaning they are used to find the optimal solution(s) to a given computational problem that maximizes or minimizes a particular function .
The number of initial solutions, the proportion of solutions retained to build parents, the way that childrens are computed from parents are parameters which can be changed but the global idea of genetic algorithm
is to defined new solutions by "mutations".
Control-System Optimization Using Genetic Algorithms
. J Guid Control Dyn 1992; 15:735-740.
The schema theorem marks a watershed moment in the understanding of genetic algorithms
as well as evolution in general.
Luengas-Contreras, "Control of diversity in genetic algorithms
using multimodal strategies", Vision electronica, algo mas que un estado solido, vol.
We can also summarize the advantages of using GA to solve the vehicle routing problem [21-22]: (i) the Genetic Algorithms
are based on the problem codification and support a wide variety of computational program; (ii) the Genetic Algorithms
provide solution near to the optimal solution even when there are unknown methods to solve the problem; (iii) the searching for the solution is performed in a population of individuals and not only at one point at a time; (iv) GA is robust and can solve a wide range of hard problems in a reliable and fast way; (v) they are easy to implement and flexible to add modifications; (vi) they are easy to combine with another methods and algorithms.
Square function, absolute function and a function for minimizing a linear combination of the equations are used to transform the equations system into a suitable problem for genetic algorithms
"A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
This study aimed to propose the use of artificial intelligence via artificial neural networks and genetic algorithms
in the simulation of oat grain yield and optimization of seeding density, respectively, in the main succession systems of southern Brazil.
The optimization method that was improved in this work is the genetic algorithm
. This metaheuristic has some parameters, such as mutation percentage, gap generation, crossing, and selection, and each parameter has an internal behavior that contributes to the complete genetic algorithm
to solve any solution [9-11].
. Genetic algorithm
(GA) is a kind of common optimization algorithm which can draw lessons from the evolutionary laws of the biological world .