scholarly journals A new culture medium based on genetic algorithms for Isochrysis galbana production relevant to hatcheries

2021 ◽  
Author(s):  
J. Camacho-Rodríguez ◽  
J. J. Gallardo-Rodríguez ◽  
M. C. Cerón-García ◽  
F. García-Camacho ◽  
E. Molina-Grima
Keyword(s):  
Genetic Algorithm ◽  
Fatty Acids ◽  
Genetic Algorithms ◽  
Culture Medium ◽  
Isochrysis Galbana ◽  
Nutrient Content ◽  
The Other ◽  
Commercial Formulation ◽  
Reference Medium ◽  
Seven Generations

AbstractThe nutrient content of a commercial seawater culture medium for growing the microalga Isochrysis galbana was optimized using a stochastic strategy based on genetic algorithms. For this, 210 experiments spread over seven generations were carried out. This strategy reduced the number of assays by more than 90% compared to a factorial design involving the optimization of twelve nutrients simultaneously. The optimized medium outperformed the reference medium in all aspects. The genetic algorithm strategy achieved a polyunsaturated fatty acids (PUFAs) productivity of 7.8 mg L−1 day−1 in a continuous culture of I. galbana, corresponding to an increase of 15% compared to the commercial formulation. Carotenoids, on the other hand, increased by 50% d.w. In addition, PUFA yields were significantly improved, which allowed us to reduce the requirement of several nutrients, for instance, N (25%), Mo (20%), Mn (60%), Co (60%), and Cu (60%).

scholarly journals Novel Method Using Crossover (Genetic Algorithms) With Matrix Technique to Modifying Ciphering by Using Playfair

2013 ◽  
Vol 6 (3) ◽  
pp. 97-106
Author(s):  
Mohammed Sami Mohammed
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
The Other ◽  
Plain Text ◽  
Cipher Text ◽  
One Step ◽  
Novel Method ◽  
Get Complex ◽  
New Generation

Two techniques combined with each other, to get complex one. One from technique of genetic algorithm (GA) and the other is PlayFair cipher method, in this research using one step of Genetic Algorithm (GA) which called Crossover to make offspring of two parents (characters) to get one or two new character by using these techniques then using PlayFair technique to cipher text (plain text). So the person who wants to break code, two techniques must know.This research is a novel method of ciphering by getting a new generation of offspring from two characters, when we give a new theory of symbols as mention in research.

Backcalculation Analysis of Pavement-Layer Moduli Using Genetic Algorithms

1997 ◽  
Vol 1570 (1) ◽  
pp. 134-142 ◽  
Author(s):  
T. F. Fwa ◽  
C. Y. Tan ◽  
W. T. Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Performance Evaluation ◽  
Objective Function ◽  
The Other ◽  
Local Minima ◽  
Structural Composition ◽  
Genetic Algorithm Approach ◽  
Solution Surface ◽  
Surface Deflections

Most existing iterative backcalculation programs for pavement layer moduli arrive at their solutions by minimizing an objective function related to the differences between computed and measured surface deflections. Unfortunately, the solution surface of the backcalculation problem of pavement-layer moduli is known to contain many local minima. A potentially good backcalculation procedure would be one that has a strong global search ability to overcome the problem of local minima. The genetic algorithm (GA) is a technique that satisfies this requirement. The development of a backcalculation program known as NUS-GABACK using the genetic-algorithm approach is presented, along with the formulation and operations of the program. A detailed performance evaluation of the GA-based method is made against four other programs by solving five backcalculation problems with different structural composition. It was found that NUS-GABACK performed comparably well against the other programs and demonstrated consistency in the accuracies of backcalculated moduli.

Mathematical Modeling and Genetic Algorithms for Product Sequencing in a Cellular System

2014 ◽  
pp. 31-47 ◽  
Author(s):  
Gürsel A. Süer ◽  
Fatih Yarimoglu
Keyword(s):  
Mathematical Modeling ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
The Other ◽  
Scheduling Problem ◽  
Optimal Result ◽  
Manufacturing Environment ◽  
Reasonable Time ◽  
Sequencing Problem

This chapter considers a product-sequencing problem in a synchronized manufacturing environment, which is using a uniform time bucket approach for synchronization. This problem has been observed in a jewelry manufacturing company and is valid in other labor-intensive cellular environments. The scheduling problem handled has two aspects: first, determining manpower allocation; second, sequencing the products in order to minimize the number of periods where available manpower is exceeded. The number of operators needed in a time bucket may exceed the available manpower level as different products have different manpower requirements for different processes. A mathematical model is developed for the manpower allocation part of the problem. To perform product sequencing, two methods are used, namely mathematical modeling and genetic algorithm. A new five-phase GA approach is proposed, and the results show that it outperforms the classical GA. Several experiments have been conducted to find better GA parameters as well. Finally, GA results are compared with mathematical model results. Mathematical Modeling finds optimal result in a reasonable time for small problems. On the other hand, for the bigger problems, genetic algorithm is a feasible approach to use.

scholarly journals SIMULATION OF PSO BASED APPROACH FOR CMOL CELL ASSIGNMENT PROBLEM

2015 ◽  
Vol 3 (5) ◽  
pp. 1-12
Author(s):  
Prateek Shrivastava ◽  
Khemraj Deshmukh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Assignment Problem ◽  
Optimization Technique ◽  
Particle Swarm ◽  
The Other ◽  
Swarm Optimization ◽  
Cell Assignment

Particle swarm optimization (PSO) approach is used over genetic algorithms (GAS) to solve many of the same kinds of problems. This optimization technique does not suffer, however, from some of GA’s difficulties; interaction in the group enhances rather than detracts from progress toward the solution. Further, a particle swarm system has memory, which the genetic algorithm does not have. In particle swarm optimization, individuals who fly past optima are tugged to return toward them; knowledge of good solutions is retained by all particles. The genetic algorithm works with the concept of chromosomes having gene where each gene act as a block of one solution. This is totally based on the solution which is followed by crossover and then mutation and finally reaches to fitness. The best fitness will be considered as a result and implemented in the practical area. Due to some drawbacks and problems exist in the genetic algorithm implemented, scientists moved to the other algorithm technique which is apparently based on the flock of birds moving to the target. This effectively overcome the shortcomings of GA and provides better fitness solutions to implement in the circuit.

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

Chlorella vulgaris as a Source of Essential Fatty Acids and Micronutrients: A Brief Commentary

2017 ◽  
Vol 10 (1) ◽  
pp. 92-99 ◽  
Author(s):  
Hércules Rezende Freitas
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Culture Medium ◽  
Essential Fatty Acids ◽  
Total Lipid Content ◽  
Dietary Quality ◽  
Negative Effects ◽  
Algae Biomass ◽  
Dietary Consumption ◽  
Acid Consumption

Polyunsaturated fatty acids (PUFAs) comprise about 35-40% of the total lipid content from green algaeChlorella, reaching up to 24% linoleic acid and 27% α-linolenic acid inC. vulgaris. Also, microalgae nutrient composition may be modulated by changes in the culture medium, increasing fatty acid and microelement concentrations in the algae biomass. PUFAs, such as α-linolenic (n-3) and linoleic (n-6) acids, as well as its derivatives, are considered essential for dietary consumption, and their ability to regulate body chemistry has been recently explored in depth. A balanced fatty acid consumption is shown to counteract the negative effects of western diets, such as chronic inflammation and glucose intolerance. In this brief commentary, technological and practical uses ofC. vulgarisare explored as means to improve dietary quality and, ultimately, human health.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Effect of Dietary Hazelnut Peels on the Contents of Fatty Acids, Cholesterol, Tocopherols, and on the Shelf-Life of Ripened Ewe Cheese

Antioxidants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 538
Author(s):  
Vita Maria Marino ◽  
Teresa Rapisarda ◽  
Margherita Caccamo ◽  
Bernardo Valenti ◽  
Alessandro Priolo ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Dry Matter ◽  
Tocopherol Content ◽  
The Other ◽  
Cholesterol Oxidation ◽  
Cholesterol Levels ◽  
Bulk Milk ◽  
Chocolate Industry ◽  
Experimental Group ◽  
Effect Of Feeding

Hazelnut peel (HNP), a by-product from the chocolate industry, is considered to be a suitable ingredient to be included in the diet of ruminants. This study aimed to evaluate the effect of feeding dairy ewes with a diet containing HNP on ripened cheese quality, including fatty acid (FA) profile, cholesterol, and tocopherol content, as well as stability during storage under commercial conditions. In total, 10 experimental cheeses were produced with bulk milk obtained from ewes fed a commercial concentrate (C group; n = 5) or a concentrate containing 36% HNP in dry matter (HNP group; n = 5). After 40 days of aging, each cheese was sub-sampled into three slices: one was analyzed immediately (C0 and HNP0), and the other two were refrigerated and analyzed after seven days (C7 and HNP7) and 14 days (C14 and HNP14), respectively. Compared to C, HNP cheese had more than twice as many tocopherols and mono-unsaturated FA and respectively 38% and 24% less of cholesterol and saturated FA. Tocopherols and cholesterol levels remained rather stable up to 14 days of storage regardless of the experimental group, suggesting no cholesterol oxidation. Therefore, the inclusion of HNP in ewe diets could be a valid resource to produce cheese with a healthier lipid profile and higher tocopherols content.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

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