scholarly journals Optimasi Asupan GGL Ideal Pada Usia Produktif Dengan Algoritma Genetika

2020 ◽  
Vol 4 (2) ◽  
pp. 145
Author(s):  
Anita Sindar RM Sinaga
Keyword(s):  
Fast Food ◽  
Eating Habits ◽  
Selection Process ◽  
Fitness Function ◽  
Optimal Solution ◽  
Adaptive Method ◽  
A Value ◽  
Candidate Solution ◽  
Fitness Value ◽  
Reproduction Process

<table border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p><em>Financial security</em><em> </em><em>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.</em><em> </em><em>The best chromosomes  offSpring 10 Fitness 12737.34.</em></p></td></tr></tbody></table>

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

scholarly journals Particle swarm optimization to produce optimal solution

2018 ◽  
Vol 7 (1.7) ◽  
pp. 210 ◽  
Author(s):  
C Saranya Jothi ◽  
V Usha ◽  
R Nithya
Keyword(s):  
Particle Swarm Optimization ◽  
Test Data ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Optimal Test ◽  
Swarm Optimization ◽  
Test Information ◽  
Fitness Value

Search-Based Software Testing is the utilization of a meta-heuristic improving scan procedure for the programmed age of test information. Particle Swarm Optimization (PSO) is one of those technique. It can be used in testing to generate optimal test data solution based on an objective function that utilises branch coverage as criteria. Software under test is given as input to the algorithm. The problem becomes a minimization problem where our aim is to obtain test data with minimum fitness value. This is called the ideal test information for the given programming under test. PSO algorithm is found to outperform most of the optimization techniques by finding least value for fitness function. The algorithm is applied to various software under tests and checked whether it can produce optimal test data. Parameters are tuned so as to obtain better results.

scholarly journals GenHap: A novel computational method based on genetic algorithms for haplotype assembly

2017 ◽  
Author(s):  
Andrea Tangherloni ◽  
Simone Spolaor ◽  
Leonardo Rundo ◽  
Marco S Nobile ◽  
Ivan Merelli ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Selection Process ◽  
Fitness Function ◽  
Computational Method ◽  
Nucleotide Polymorphisms ◽  
Optimization Strategy ◽  
Genetic Operators ◽  
Chromosome Conformation ◽  
Haplotype Assembly ◽  
Fitness Value

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

scholarly journals Optimisation of Rice Fertiliser Composition using Genetic Algorithms

2019 ◽  
Vol 2 (2) ◽  
pp. 72
Author(s):  
Retno Dewi Anissa ◽  
Wayan Firdaus Mahmudy ◽  
Agus Wahyu Widodo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Process ◽  
Test Results ◽  
Random Mutation ◽  
Rice Quality ◽  
Food Scarcity ◽  
Fitness Value ◽  
Reproduction Process ◽  
Tournament Selection

There are so many problems with food scarcity. One of them is not too good rice quality. So, an enhancement in rice production through an optimal fertiliser composition. Genetic algorithm is used to optimise the composition for a more affordable price. The process of genetic algorithm is done by using a representation of a real code chromosome. The reproduction process using a one-cut point crossover and random mutation, while for the selection using binary tournament selection process for each chromosome. The test results showed the optimum results are obtained on the size of the population of 10, the crossover rate of 0.9 and the mutation rate of 0.1. The amount of generation is 10 with the best fitness value is generated is equal to 1,603.

scholarly journals GenHap: A novel computational method based on genetic algorithms for haplotype assembly

2017 ◽  
Author(s):  
Andrea Tangherloni ◽  
Simone Spolaor ◽  
Leonardo Rundo ◽  
Marco S Nobile ◽  
Ivan Merelli ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Selection Process ◽  
Fitness Function ◽  
Computational Method ◽  
Nucleotide Polymorphisms ◽  
Optimization Strategy ◽  
Genetic Operators ◽  
Chromosome Conformation ◽  
Haplotype Assembly ◽  
Fitness Value

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

Optimization the Combined Intake of Sugar-Salt-Fat Using Genetich Algorithm

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 85
Author(s):  
Preddy Marpaung ◽  
Arjon Samuel Sitio ◽  
Anita Sindar RM Sinaga
Keyword(s):  
Fast Food ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Daily Food ◽  
Fitness Value ◽  
Main Components ◽  
Uncontrolled Eating ◽  
The Mathematical Model ◽  
The Given ◽  
Crossover And Mutation

Salt sugar and fat intake in the human body in daily food patterns. To avoid non-communicable diseases, it is necessary to optimize food intake levels. Productive age, in this case between 15-55 years have uncontrolled eating patterns to choose fast food. If this is not monitored, it will cause various diseases. The importance of evolutionary algorithms for solving complex problems that are difficult to solve analytically can use mathematical models. The purpose of this study is to determine the composition of the ideal intake for productive generations with a minimum cost that must meet the minimum threshold for each nutritional component. Based on the mathematical model built, an analysis is carried out to find the optimal solution. Optimization is an effort or activity to get the best results with the given requirements. There are 5 main components in the Genetic Algorithm, namely Fitness, Value, Selection, Crossover, and Mutation. The results showed optimization of the intake of selected chromosomes as the best results, obtained on chromosome offSpring: 10, Fitness value: 12737.34.

scholarly journals Influence of COVID-19 on lifestyle behaviors in the Middle East and North Africa Region: a survey of 5896 individuals

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Mohamed Abouzid ◽  
◽  
Dina M. El-Sherif ◽  
Nael Kamel Eltewacy ◽  
Nesrine Ben Hadj Dahman ◽  
...  
Keyword(s):  
Social Media ◽  
Middle East ◽  
Food Consumption ◽  
North Africa ◽  
Fast Food ◽  
Online Survey ◽  
Eating Habits ◽  
Lifestyle Behaviors ◽  
Mena Region ◽  
The Impact

Abstract Background Coronavirus disease (COVID-19) pandemic has affected health and lifestyle behaviors of people globally. This project aims to identify the impact of COVID-19 on lifestyle behavior of individuals in the Middle East and North Africa (MENA) region during confinement. Methods We conducted an online survey in 17 countries (Egypt, Jordan, United Arab Emirates, Kuwait, Bahrain, Saudi Arabia, Oman, Qatar, Yemen, Syria, Palestine, Algeria, Morocco, Libya, Tunisia, Iraq, and Sudan) from the MENA region on August and September 2020. The questionnaire included self-reported information on lifestyle behaviors, including physical activity, eating habits, smoking, watching television, social media use and sleep before and during the pandemic. Logistic regression was performed to analyze the impact of COVID-19 on lifestyle behaviors. Results A total of 5896 participants were included in the final analysis and 62.8% were females. The BMI of the participants was 25.4 ± 5.8 kg/m2. Around 38.4% of the participants stopped practicing any physical activities during the confinement (P < 0.001), and 57.1% reported spending more than 2 h on social media (P < 0.001). There were no significant changes in smoking habits. Also, 30.9% reported an improvement in their eating habits compared with 24.8% reported worsening of their eating habits. Fast-food consumption decreased significantly in 48.8% of the study population. This direct/indirect exposure to COVID-19 was associated with an increased consumption of carbohydrates (OR = 1.09; 95% CI = 1.02–1.17; P = 0.01), egg (OR = 1.08; 95% CI = 1.02–1.16; P = 0.01), sugar (OR = 1.09; 95% CI = 1.02–1.16; P = 0.02), meat, and poultry (OR = 1.13; 95% CI = 1.06–1.20; P < 0.01). There was also associated increase in hours spent on watching television (OR = 1.07; 95% CI = 1.02–1.12; P < 0.01) and social media (OR = 1.09; 95% CI = 1.01–1.18; P = 0.03). However, our results showed a reduction in sleeping hours among those exposed to COVID-19 infection (OR = 0.85; 95% CI = 0.77–0.94; P < 0.01). Conclusions The COVID-19 pandemic was associated with an increase in food consumption and sedentary life. Being exposed to COVID-19 by direct infection or through an infected household is a significant predictor of amplifying these changes. Public health interventions are needed to address healthy lifestyle behaviors during and after the COVID-19 pandemic.

Distribution Network Reactive Power Planning Based on Improved Evolutionary Strategy Theory

2013 ◽  
Vol 732-733 ◽  
pp. 1023-1028
Author(s):  
Si Qing Sheng ◽  
Xing Li ◽  
Yang Lu
Keyword(s):  
Reactive Power ◽  
Distribution Network ◽  
Load Condition ◽  
Optimal Solution ◽  
Evolution Strategy ◽  
Evolutionary Strategy ◽  
Initial Population ◽  
Fitness Value ◽  
Cauchy Mutation ◽  
Reactive Power Planning

In this paper a distribution network reactive power planning mathematical model was established, taking the minimized sum of electrical energy loss at the different load operation modes and the investment for reactive power compensation equipments as objective function to solve the planning question respectively and taking the transformer tap as equality constraint. The evolution strategy is improved, The Euclidean distance is introduced into the formation of the initial population, and the initial population under the max load operation mode is based on the optimal solution of the min load condition. The Cauchy mutation and variation coefficient are introduced into the evolution strategy method. By means of improvement of fitness to ensure diversity of population in early and accuracy of the fitness value.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

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