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

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 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.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN POSISI PEMAIN IDEAL DALAM TIM SEPAK BOLA DENGAN METODE ALGORITMA GENETIKA

Compiler ◽  
2012 ◽  
Vol 1 (2) ◽  
Author(s):  
Hizkia Alprianta ◽  
Anton Setiawan Honggowibowo ◽  
Yuliani Indrianingsih
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
User Interface ◽  
Interface Design ◽  
Selection Process ◽  
Decision Making Process ◽  
Media Processing ◽  
Ideal Position ◽  
The Right ◽  
The Ideal

So far, there are coaches who are less precise in determining the ideal position of the player as it only relies on instinct and the ego of the players so that there is still a coach who has not been able to objectively assess the players.By utilizing the method of Genetic Algorithm as Decision Support System (DSS) in the process of determining the ideal position of a player who uses several criteria (multicriteria) to choose a proper player. DSS is helping coach in making the right decisions and Genetic Algorithm is used as a model for multicriteria weighting in the selection process. This application was built with tools Borland Delphi (7.0) as the user interface design and media processing PostgreSQL as its database.            Based on these results we can conclude that this application expected to assist the coaches in the decision making process and can change the appraisal of which are subjective to more objective, to determine the ideal position for a player, can determine the best position of each position of a number of players and the expected results of the Genetic Algorithm on the system constructed in accordance with the results of manual calculations.

Improved Vibration Monitoring Through Optimization of Sensor Placements

2000 ◽  
Author(s):  
Mark D. Sensmeier ◽  
Kurt L. Nichol
Keyword(s):  
Genetic Algorithm ◽  
Selection Process ◽  
Dynamic Test ◽  
Software Tool ◽  
Vibration Monitoring ◽  
Optimization Approach ◽  
Algorithm Optimization ◽  
Turbine Engine ◽  
Mode Identification ◽  
Engine Components

A PC-based software tool has been developed which optimizes the placement of sensors for vibration monitoring. This tool, called Blade-OPS, incorporates a methodology that allows the instrumentation design engineer to make tradeoffs between mode identification, mode visibility, data integrity and geometry. It uses a genetic algorithm optimization approach that simulates the natural selection process to develop an optimum design. For the blade considered here, several instrumentation configurations were selected which yield an improved fitness rating relative to the baseline sensor locations which were selected without using rigorous optimization approach. Application of this capability is not limited to turbine engine components, but will be useful for any dynamic test where instrumentation is limited.

A Hybrid Feature Selection Based on Mutual Information and Genetic Algorithm

2017 ◽  
Vol 7 (1) ◽  
pp. 214
Author(s):  
Yuan-Dong Lan
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mutual Information ◽  
Learning Algorithm ◽  
Selection Process ◽  
Optimal Subset ◽  
Benchmark Datasets ◽  
Two Phases ◽  
Feature Dimension ◽  
Necessary And Sufficient

Feature selection aims to choose an optimal subset of features that are necessary and sufficient to improve the generalization performance and the running efficiency of the learning algorithm. To get the optimal subset in the feature selection process, a hybrid feature selection based on mutual information and genetic algorithm is proposed in this paper. In order to make full use of the advantages of filter and wrapper model, the algorithm is divided into two phases: the filter phase and the wrapper phase. In the filter phase, this algorithm first uses the mutual information to sort the feature, and provides the heuristic information for the subsequent genetic algorithm, to accelerate the search process of the genetic algorithm. In the wrapper phase, using the genetic algorithm as the search strategy, considering the performance of the classifier and dimension of subset as an evaluation criterion, search the best subset of features. Experimental results on benchmark datasets show that the proposed algorithm has higher classification accuracy and smaller feature dimension, and its running time is less than the time of using genetic algorithm.

Emergency Dynamic Alliance Partner Selection Based on Adaptive Genetic Algorithm

2014 ◽  
Vol 668-669 ◽  
pp. 1621-1624 ◽  
Author(s):  
Min Hu
Keyword(s):  
Genetic Algorithm ◽  
Value Chain ◽  
Selection Process ◽  
Partner Selection ◽  
Task Value ◽  
Adaptive Genetic Algorithm ◽  
Alliance Partner ◽  
Dynamic Alliance ◽  
Analytical Perspective ◽  
Overall Effectiveness

Emergency mobilization alliance partner selection process is longitudinally choice of the value chain to achieve a task. Value of subtasks coefficient has been discussed. Depending on the difficulty of the problem and analytical perspective, the model of emergency mobilization alliance partner selection is given to maximize the overall effectiveness of the emergency mobilization. The choice of partner selection using adaptive genetic algorithm is made and the comparison with other methods has been analyzed.

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