Implementation of a genetic algorithm using a computer application based on neural and evolutionary computing to obtain the best adapted chromosome
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Keywords
genetic algorithm, neural and evolutionary computing, chromosome
Abstract
The aim of the research is to implement a genetic algorithm using a computer application based on neural and evolutionary computing to obtain the best adapted chromosome, such development demands statistical analysis of decriptive based on genetic algorithms. I know specifically used twenty types of chromosomes that the input data will be a network in Pajek format (*.net) and the output data will mention the highest modularity partition found, in Pajek format (*.clu), and its corresponding modularity value, i.e. after chromosome selection, crossing or mutation is performed the evaluation for decoding by means of the Fitness parameter , thus selecting through the Wheel of roulette to obtain the best-adapted chromosome.
Twenty types of chromosomes transformed into Pajek (.net) format were specifically used and genetic algorithms (AG) work between the solution set of a problem called phenotype, and the set of individuals from a natural population, encoding the information of each solution into a string, usually binary, called a chromosome. The symbols that make up the chain are called genes. When the chromosome representation is done with binary digit strings it is known as a genotype. Chromosomes evolve through iterations, called generations. In each generation, chromosomes are evaluated using neural and evolutionary computing using some measure of fitness. The following generations (new chromosomes), are generated by applying the genetic operators repeatedly, these being the operators of selection, crossing, mutation and replacement, to obtain the best adapted chromosome.
This article explains the implementation of a genetic algorithm using a computer application based on neural and evolutionary computing to obtain the best adapted chromosome, which starts from a population of solutions, and based on the value of the adaptation function for each of the individuals (solutions) of that population, select the best individuals (according to that function) and combine to generate new ones. This process is repeated cyclically until a stop criterion is met.
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María de los Ángeles Rodríguez-Cevallos, Chimborazo Higher Polytechnic School (ESPOCH), Riobamba, Ecuador.
Magister in Clinical Nutrition
María José Andrade-Albán, Chimborazo Higher Polytechnic School (ESPOCH), Riobamba, Ecuador.
Master in Agroindustrial Production Management
Roberto Carlos Maldonado-Palacios, Pontifical Catholic University of Ecuador (PUCE), Quito, Ecuador.
Pontifical Catholic University of Ecuador (PUCE), Quito, Ecuador.
Cristhian Alfonso Cobos-Cevallos, National University of Chimborazo (UNACH), Riobamba, Ecuador.
Computer and Systems Engineering Student.