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.

scholarly journals Editorial for the Special Issue: Computer-Aided Manufacturing and Design

2020 ◽  
Vol 10 (16) ◽  
pp. 5650
Author(s):  
Qi Zhou ◽  
Seung-Kyum Choi ◽  
Recep M. Gorguluarslan
Keyword(s):  
Production Process ◽  
Computer Technology ◽  
Integrated Design ◽  
Manufacturing Processes ◽  
Special Issue ◽  
Direct Control ◽  
Computer Aided Manufacturing ◽  
Computer Aided ◽  
Design And Manufacturing ◽  
Computer Based

Recent advancements in computer technology have allowed designers to have direct control over the production process through the help of computer-based tools, creating the possibility of completely integrated design and manufacturing processes [...]

scholarly journals A Genetic Algorithm for Truck Dispatching in Mining

10.29007/n11t ◽  
2018 ◽  
Author(s):  
Wesley Cox ◽  
Tim French ◽  
Mark Reynolds ◽  
Lyndon While
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Finite Automata ◽  
Cost Savings ◽  
Greedy Heuristics ◽  
Significant Cost ◽  
Apply Genetic

We apply genetic algorithms (GAs) to evolve cyclic finite automata for scheduling the dispatch of trucks in mines. The GA performs well generally, and on problems which include one-lane roads, the GA was able to find solutions that utilised shovels very well, with low contention and using fewer trucks than both the widely-used linear programming DISPATCH algorithm, and commonly-used greedy heuristics. The GA provides significant cost-savings, or production increases, on problems where alternative algorithms do not adapt well.

scholarly journals Algoritma Genetika dengan Mutasi Terbatas untuk Penjadwalan Perkuliahan

2021 ◽  
Vol 21 (2) ◽  
pp. 229-242
Author(s):  
Sisferi Hikmawan
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Service Satisfaction ◽  
Mutation Operator ◽  
Manual Method ◽  
Academic Year

Abstract   In University, lecture scheduling is the most important factor in service satisfaction for students. UNISMA Bekasi still uses the manual method in scheduling lectures. Genetic Algorithms can solve scheduling with different constraints. In the proposed Genetic Algorithm, the mutation operator is changed to be a limited individual mutation and a selection feature that is adjusted to the constraints in the problem to be solved. And Genetic Algorithms with limited mutations are proven to have advantages in accommodating the constraints found in UNISMA Bekasi. The result of testing in experiments conducted on curriculum data for the Odd Semester of the Academic Year 2020/2021 using a Genetic Algorithm with mutation_individu_terbatas, namely minimum load = 0 with iterations = 10 and population = 500.   Keywords: Data Mining, Genetic Algorithm, Schedule, Mutation   Abstrak   Dalam perkuliahan, penjadwalan perkuliahan merupakan faktor paling penting dalam kepuasan pelayanan terhadap mahasiswa. UNISMA Bekasi masih menggunakan cara manual dalam penjadwalan perkuliahan. Algoritma Genetika dapat memecahkan penjadwalan dengan constraint berbeda-beda. Pada Algoritma Genetika yang diajukan, dilakukan pengubahan operator mutasi menjadi mutasi individu terbatas  dan fitur seleksi yang disesuaikan dengan constraint dalam permasalahan yang ingin dipecahkan. Dan Algoritma Genetika dengan mutasi terbatas terbukti memiliki kelebihan dalam mengakomodir permasalahan constraint yang terdapat di UNISMA Bekasi. Dihasilkan Pengujian dalam percobaan yang dilakukan terhadap data kurikulum untuk Semester Ganjil Tahun Akademik 2020/2021 dengan menggunakan Algoritma Genetika dengan mutasi_individu_terbatas yaitu beban minimum = 0 dengan iterasi = 10 dengan populasi = 500.   Kata kunci: Data Mining, Algoritma Genetika, Mutasi, Jadwal Perkuliahan

A Research on Genetic Algorithms Based on Test Paper Composition of Item Response Theory

2013 ◽  
Vol 756-759 ◽  
pp. 2625-2628
Author(s):  
Tao Sun ◽  
Yan Hua Chu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Item Response Theory ◽  
Item Response ◽  
Computer Technology ◽  
Test Generation ◽  
Test Paper ◽  
Response Theory ◽  
Adaptive Test ◽  
Test Application

Along with the computer technology in the examination of the application, the examination has become a hot topic. However, test paper is an important part of the adaptive test. According to the test generation of the objectives and requirements, this paper introduces a method based on item response theory ( IRT ) thought of genetic algorithm in the test application, thereby effectively solving the problem of intelligent test paper.

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.

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.

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.

The Role of Constraints and Human Interaction in Evolving MEMS Designs: Microresonator Case Study

2004 ◽  
Author(s):  
Raffi Kamalian ◽  
Alice M. Agogino ◽  
Hideyuki Takagi
Keyword(s):  
Genetic Algorithms ◽  
Geometric Constraints ◽  
Human Interaction ◽  
Design System ◽  
Current State ◽  
Mems Resonator ◽  
Computer Aided ◽  
Generative Methods

In this paper we review the current state of automated MEMS synthesis with a focus on generative methods. We use the design of a MEMS resonator as a case study and explore the role that geometric constraints and human interaction play in a computer-aided MEMS design system based on genetic algorithms.

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