Niched-Pareto Genetic Algorithm for Aircraft Technology Selection Process

2006 ◽  
Author(s):  
Chirag Patel ◽  
Michelle Kirby ◽  
Dimitri Mavris
Keyword(s):  
Genetic Algorithm ◽  
Selection Process ◽  
Technology Selection

Application of Genetic Algorithms in the Engine Technology Selection Process

2004 ◽  
Vol 126 (4) ◽  
pp. 693-700 ◽  
Author(s):  
Bryce Roth ◽  
Chirag Patel
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Process ◽  
Technology Selection ◽  
Algorithm Optimization ◽  
Identification Evaluation ◽  
Function Formulation ◽  
Visualization Techniques ◽  
Technology Identification ◽  
The Impact

The objective of this paper is to demonstrate the application of genetic algorithms to the engine technology selection process. The “technology identification, evaluation, and selection” method is discussed in conjunction with genetic algorithm optimization as a technique to quickly evaluate the impact of various technologies and select the subset with the highest potential payoff. Techniques used to model various aspects of engine technologies are described, with emphasis on technology constraints and their impact on the combinatorial optimization of technologies. Challenges include objective function formulation and development of models to deal with incompatibilities among different technologies. Typical results are presented for an 80-technology optimization using various visualization techniques to assist in easy interpretation of genetic algorithm results. Finally, several ideas for future development of these methods are briefly explored.

Application of Genetic Algorithms in the Engine Technology Selection Process

2003 ◽  
Author(s):  
Bryce Roth ◽  
Chirag Patel
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Process ◽  
Technology Selection ◽  
Algorithm Optimization ◽  
Identification Evaluation ◽  
Function Formulation ◽  
Visualization Techniques ◽  
Technology Identification ◽  
The Impact

The objective of this paper is to demonstrate the application of Genetic Algorithms to the engine technology selection process. The “Technology Identification, Evaluation, and Selection” method is discussed in conjunction with Genetic Algorithm optimization as a technique to quickly evaluate the impact of various technologies and select the subset with the highest potential payoff. Techniques used to model various aspects of engine technologies are described, with emphasis on technology constraints and their impact on the combinatorial optimization of technologies. Challenges include objective function formulation and development of models to deal with incompatibilities among different technologies. Typical results are presented for an 80-technology optimization using various visualization techniques to assist in easy interpretation of Genetic Algorithm results. Finally, several ideas for future development of these methods are briefly explored.

scholarly journals Technology Selection Assistance Tool for Positive Energy Districts

2021 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Andrea Gabaldón Moreno ◽  
Beril Alpagut ◽  
Cecilia Sanz Montalvillo
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Selection Process ◽  
Holistic Approach ◽  
Technology Selection ◽  
Positive Energy ◽  
Innovative Technologies ◽  
Lack Of Information ◽  
Decision Making Tool ◽  
Assistance Tool

Positive energy districts (PEDs) consist of more than three interconnected buildings that annually produce more renewable energy than what is consumed within the district boundaries. To achieve the annual surplus of energy, implementation of renewable-driven and innovative technologies is needed. However, most cities struggle in deciding what technologies are more suitable for their environment due to the lack of information and experience in a holistic approach. A decision-making tool has been developed within MAKING-CITY, with the collaboration of ATELIER project, to assist in the PED technology selection process, empowering cities with information and recommendations, in line with their district context and city objectives.

Case Study

2019 ◽  
pp. 299-311
Author(s):  
Amit Verma ◽  
Iqbaldeep Kaur ◽  
Dolly Sharma ◽  
Inderjeet Singh
Keyword(s):  
Genetic Algorithm ◽  
Information Retrieval ◽  
Selection Process ◽  
Limiting Factors ◽  
Recruitment Process ◽  
Software Environment ◽  
Major Application ◽  
Limiting Values ◽  
Taking Care

Recruitment process takes place based on needed data while certain limiting factors are ignored. The objective of the chapter is to recruit best employees while taking care of limiting factors from the cluster for resource management and scheduling. Various parameters of the recruits have been selected to find the maximum score achieved by them. Recruitment process makes a database as cluster in the software environment perform the information retrieval on the database and then perform data mining using genetic algorithm while taking care of the positive values in contrast to limiting values received from the database. A bigger level recruitment process finds required values of a person, so negative points are ignored earlier in the recruitment process because there is no direct way to compare them. Genetic algorithm will create output in the form of chromosomal form. Again, apply information retrieval to get actual output. Major application of this process is that it will improve the selection process of candidates to a higher level of perfection in less time.

scholarly journals Selecting E-Mobility Transport Solutions for Mountain Rescue Operations

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6613
Author(s):  
Christian Wankmüller ◽  
Maximilian Kunovjanek ◽  
Robert Gennaro Sposato ◽  
Gerald Reiner
Keyword(s):  
Selection Process ◽  
Technology Selection ◽  
Operational Performance ◽  
Validity Study ◽  
Research Approach ◽  
Qualitative And Quantitative ◽  
Rescue Workers ◽  
Mountain Rescue ◽  
Best Worst Scaling ◽  
Study Results

This study introduces e-mobility for humanitarian purposes and presents the first investigation of innovative e-mobility transport solutions (e.g., e-bike, e-stretcher, and drone) for mountain rescue. In practice, it is largely unclear which e-mobility transport solutions might be suitable and what selection attributes are to be considered. The subsequent study supports the technology selection process by identifying and measuring relevant selection attributes to facilitate the adoption of e-mobility in this domain. For the purpose of this study, a multi-method research approach that combines qualitative and quantitative elements was applied. In the first step, results of a systematic search for attributes in literature were combined with inputs gained from unstructured expert interviews and discussions. The perceived importance of the identified selection attributes was then measured by analyzing survey data of 341 rescue workers using the best-worst scaling methodology. Finally, the results were reiterated in another expert discussion to assess their overall validity. Study results indicate that e-mobility transport solutions need to primarily enhance operational performance and support the safety of mountain rescue personnel. Surprisingly, economic and sustainability aspects are less of an issue in the process of technology selection.

scholarly journals Optimalisasi Penyelesaian Knapsack Problem Dengan Algoritma Genetika

2016 ◽  
pp. 182
Author(s):  
I Wayan Supriana
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Everyday Life ◽  
Knapsack Problem ◽  
Selection Process ◽  
Optimization Algorithms ◽  
Evolutionary Process ◽  
Knapsack Problems ◽  
Theory Of Evolution ◽  
Selection Of

Knapsack problems is a problem that often we encounter in everyday life. Knapsack problem itself is a problem where a person faced with the problems of optimization on the selection of objects that can be inserted into the container which has limited space or capacity. Problems knapsack problem can be solved by various optimization algorithms, one of which uses a genetic algorithm. Genetic algorithms in solving problems mimicking the theory of evolution of living creatures. The components of the genetic algorithm is composed of a population consisting of a collection of individuals who are candidates for the solution of problems knapsack. The process of evolution goes dimulasi of the selection process, crossovers and mutations in each individual in order to obtain a new population. The evolutionary process will be repeated until it meets the criteria o f an optimum of the resulting solution. The problems highlighted in this research is how to resolve the problem by applying a genetic algorithm knapsack. The results obtained by the testing of the system is built, that the knapsack problem can optimize the placement of goods in containers or capacity available. Optimizing the knapsack problem can be maximized with the appropriate input parameters.

Hybrid System based on Rough Sets and Genetic Algorithms for Medical Data Classifications

2013 ◽  
Vol 3 (4) ◽  
pp. 31-46 ◽  
Author(s):  
Hanaa Ismail Elshazly ◽  
Ahmad Taher Azar ◽  
Aboul Ella Hassanien ◽  
Abeer Mohamed Elkorany
Keyword(s):  
Genetic Algorithm ◽  
Hybrid System ◽  
Selection Process ◽  
Decision Rules ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Biomedical Data ◽  
Hybrid Techniques ◽  
Learning Techniques

Computational intelligence provides the biomedical domain by a significant support. The application of machine learning techniques in medical applications have been evolved from the physician needs. Screening, medical images, pattern classification, prognosis are some examples of health care support systems. Typically medical data has its own characteristics such as huge size and features, continuous and real attributes that refer to patients' investigations. Therefore, discretization and feature selection process are considered a key issue in improving the extracted knowledge from patients' investigations records. In this paper, a hybrid system that integrates Rough Set (RS) and Genetic Algorithm (GA) is presented for the efficient classification of medical data sets of different sizes and dimensionalities. Genetic Algorithm is applied with the aim of reducing the dimension of medical datasets and RS decision rules were used for efficient classification. Furthermore, the proposed system applies the Entropy Gain Information (EI) for discretization process. Four biomedical data sets are tested by the proposed system (EI-GA-RS), and the highest score was obtained through three different datasets. Other different hybrid techniques shared the proposed technique the highest accuracy but the proposed system preserves its place as one of the highest results systems four three different sets. EI as discretization technique also is a common part for the best results in the mentioned datasets while RS as an evaluator realized the best results in three different data sets.

Conceptual Spacecraft Design Using a Genetic Algorithm Trade Selection Process

1999 ◽  
Vol 36 (1) ◽  
pp. 200-208 ◽  
Author(s):  
Todd Mosher
Keyword(s):  
Genetic Algorithm ◽  
Selection Process ◽  
Spacecraft Design
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