scholarly journals Genetic Algorithm for Teaching Distribution based on Lecturers’ Expertise

2019 ◽  
pp. 297-304
Author(s):  
Rizki Agung Pambudi ◽  
Wahyuni Lubis ◽  
Firhad Rinaldi Saputra ◽  
Hanif Prasetyo Maulidina ◽  
Vivi Nur Wijayaningrum
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Learning Process ◽  
Teaching And Learning ◽  
Optimization Methods ◽  
Test Results ◽  
Optimal Parameters ◽  
Crossover Rate ◽  
Fitness Value

The teaching distribution for lecturers based on their expertise is very important in the teaching and learning process. Lecturers who teach a course that is in accordance with their interests and abilities will make it easier for them to deliver material in class. In addition, students will also be easier to accept the material presented. However, in reality, the teaching distribution is often not in accordance with the expertise of the lecturer so that the lecturers are not optimal in providing material to their students. This problem can be solved using optimization methods such as the genetic algorithm. This study offers a solution for teaching distribution that focuses on the interest of each lecturer by considering the order of priorities. The optimal parameters of the test results are crossover rate (cr) = 0.6, mutation rate (mr) = 0.4, number of generations = 40, and population size = 15. Genetic algorithm is proven to be able to produce teaching distribution solutions with a relatively high fitness value at 4903.3.

scholarly journals Improving Cloud Data Encryption Using Customized Genetic Algorithm

2020 ◽  
Vol 12 (6) ◽  
pp. 46-63
Author(s):  
Muhammad Junaid Arshad ◽  
◽  
Muhammad Umair ◽  
Saima Munawar ◽  
Nasir Naveed ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Data Privacy ◽  
Data Encryption ◽  
Local Information ◽  
Sensitive Information ◽  
Secret Key ◽  
Security Breaches ◽  
Size Number

Data Encryption is widely utilized for ensuring data privacy, integrity, and confidentiality. Nowadays, a large volume of data is uploaded to the cloud, which increases its vulnerability and adds to security breaches. These security breaches include circumstances where sensitive information is being exposed to third parties or any access to sensitive information by unauthorized personnel. The objective of this research is to propose a method for improving encryption by customizing the genetic algorithm (GA) with added steps of encryption. These added steps of encryption include the data being processed with local information (chromosome's value calculated with computer-generated random bits without human intervention). The improvement in the randomness of the key generated is based on altering the population size, number of generations, and mutation rate. The first step of encrypting is to convert sample data into binary form. Once the encryption process is complete, this binary result is converted back to get the encrypted data or cipher-text. Foremost, the GA operators (population size, number of generations, and mutation rate) are changed to determine the optimal values of each operator to bring forth a random key in the minimum possible time, then local intelligence is headed in the algorithm to further improve the outcomes. Local Intelligence consists of local information and a random bit generated in each iteration. Local Information is the current value of a parent in each iteration at the gene level. Both local information and random bit are then applied in a mathematical pattern to generate a randomized key. The local intelligence-based algorithm can operate better in terms of time with the same degree of randomness that is generated with the conventional GA technique. The result showed that the proposed method is at least 80% more efficient in terms of time while generating the secret key with the same randomness level as generated by a conventional GA. Therefore, when large data are intended to be encrypted, then using local intelligence can demonstrate to be better utilized time.

scholarly journals Using Genetic Algorithm to Break Knapsack Cipher with Sequence Size 16

2014 ◽  
Vol 41 (1) ◽  
pp. 55-59
Author(s):  
Safaa Omran ◽  
Ali Al_Khalid ◽  
Israa Ali
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Search Methods ◽  
Networked System ◽  
Secret Communication ◽  
Fields Of Study

With the growth of networked system and applications such as eCommerce, the demand for effective internetsecurity is increasing. Cryptology is the science and study of systems for secret communication. It consists of twocomplementary fields of study: cryptography and cryptanalysis.The genetic algorithm is one of the search methods, whichfinds the optimal solution. It is one of the methods, which is used to decrypt cipher.This work focuses on using GeneticAlgorithms to cryptanalyse knapsack cipher. The knapsack cipher is with a knapsack sequence of size 16 to encrypt twocharacters together. Different values of parameters have been used: Population size, mutation rate, number of generation.

THE ROLE OF MUTATION AND POPULATION SIZE IN GENETIC ALGORITHMS APPLIED TO PHYSICS PROBLEMS

2001 ◽  
Vol 12 (09) ◽  
pp. 1345-1355 ◽  
Author(s):  
E. BELMONT-MORENO
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Population Size ◽  
Mutation Rate ◽  
Test Problems ◽  
Optimum Performance ◽  
Analytical Expressions ◽  
Standard Genetic Algorithm ◽  
Physics Problems

A standard Genetic Algorithm is applied to a set of test problems, three of them taken from physics and the rest analytical expressions explicitly constructed to test search procedures. The relation between mutation rate and population size in the search for optimum performance is obtained showing similar behavior in these problems.

scholarly journals Algoritma Genetika untuk Optimasi Komposisi Makanan Bagi Penderita Hipertensi

2019 ◽  
Vol 7 (1) ◽  
pp. 1-6
Author(s):  
Anggi Mahadika Purnomo ◽  
Davia Werdiastu ◽  
Talitha Raissa ◽  
Restu Widodo ◽  
Vivi Nur Wijayaningrum
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Food Composition ◽  
Local Optimum ◽  
Activity Type ◽  
Search Results ◽  
Reproduction Method ◽  
The Right ◽  
Average Fitness

Hypertension can be prevented and handled by eating nutritious foods with the right composition. The genetic algorithm can be used to optimize the food composition for people with hypertension. Data used include sex, age, weight, height, activity type, stress level, and patient hypertension level. This study uses a reproduction method that is good enough to be applied to integer chromosome representations so that the search results provided are not local optimum solutions. The testing results show that the best genetic algorithm parameters are as follows population size is 15 with average fitness 20.97, the generation number is 40 with average fitness 50.10, and combination crossover rate and mutation rate are 0.3 and 0.7 with average fitness 41.67. The solution obtained is the optimal food composition for people with hypertension.

The Application of Genetic Algorithm on Optimization Problem for Mayonnaise Compositions

2011 ◽  
Vol 480-481 ◽  
pp. 219-224
Author(s):  
Zhi Yang Luo ◽  
Hong Xia Zhao ◽  
Xin Yuan ◽  
Yuan Li
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Function Optimization ◽  
Solve Optimization Problem ◽  
Iteration Number

For some function optimization problems of non-linear, multi-model and multi-objective, they are difficult to solve by other optimization methods, however, genetic algorithm is easy to find good results, so a kind of optimization problem for mayonnaise compositions based on genetic algorithm is introduced. This termination condition is selected according to the iteration number of maximum generation, the optimal solution of last generation in the evolution is the final result with genetic algorithm to solve optimization problem. The population size is 20, crossover rate is 0.7, and mutation rate is 0.04. Via the evolution of 100 generations, the optimization solution is gotten, which has certain guiding significance for the production.

scholarly journals A phylogenetic estimator of effective population size or mutation rate.

Genetics ◽  
1994 ◽  
Vol 136 (2) ◽  
pp. 685-692 ◽  
Author(s):  
Y X Fu
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Mutation Rate ◽  
Dna Sequences ◽  
High Efficiency ◽  
Minimum Variance ◽  
Population Subdivision ◽  
Effective Population ◽  
Fisher Model ◽  
Mitochondrial Sequences

Abstract A new estimator of the essential parameter theta = 4Ne mu from DNA polymorphism data is developed under the neutral Wright-Fisher model without recombination and population subdivision, where Ne is the effective population size and mu is the mutation rate per locus per generation. The new estimator has a variance only slightly larger than the minimum variance of all possible unbiased estimators of the parameter and is substantially smaller than that of any existing estimator. The high efficiency of the new estimator is achieved by making full use of phylogenetic information in a sample of DNA sequences from a population. An example of estimating theta by the new method is presented using the mitochondrial sequences from an American Indian population.

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

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