Search Results - (( developing set genetic algorithm ) OR ( java simulation optimization algorithm ))

Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…Precisely, the second set revealed that the proposed genetic medoid based algorithms with both DB and VRC fitness functions produced more accurate results compared with the genetic means based algorithms in terms of the Fscore. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…The simulation is implemented with iFogSim and java programming language. …”
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Solving transcendental equation using genetic algorithm / Masitah Hambari by Masitah , Hambari

“…Genetic Algorithm is used to find the roots or set of optimal solution that satisfy the equation. …”
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition by Yahya, Anwar Ali, Mahmod, Ramlan, Ramli, Abd Rahman

“…In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks' random variables. …”
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A Micro-Genetic Algorithm Approach for Soft Constraint Satisfaction Problem in University Course Scheduling by Abd. Halim, Bohadean @ Bohari

“…The Micro Genetic Algorithm proposed has been tested in a test comparison with the Standard Genetic algorithm and the Guided Search Genetic algorithm as a benchmark. …”
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Development of heuristic methods based on genetic algorithm (GA) for solving vehicle routing problem by Ismail, Zuhaimy, Nurhadi, Irhamah, Zainuddin, Zaitul Marlizawati

“…New algorithms, journal papers and computerized system were also developed. …”
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Development of genetic algorithm-based fuzzy rules design for metal cutting data selection by Wong, S.V, Hamouda, A.M.S

“…The development of a Fuzzy Genetic Optimization algorithm is presented and discussed. …”
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Optimisation of multi-stage production-inspection stations using genetic algorithm by Hassan, Azmi, Pham, Duc Trung

“…The setting of the parameters for the genetic operators and the ways of initialising the solution population are also described. …”
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Genetic algorithms for VLSI micro-Cell layout area optimization based on binary tree by Hasliza, A. Rahim@Samsuddin, Ab Al-Hadi, Ab Rahman, R. Badlishah, Ahmad, 'Aini Syuhada, Md Zain, M.I., Ahmad, Wan Nur Suryani Firuz, Wan Arrifin

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Multi-Objective Optimization of Solar Powered Irrigation System by Using Genetic Algorithm by Mohd Tholaat, Muhammad Ali Husaini

“…Genetic Algorithm is a common technique and easy to work with but it has yet to be the best metaheuristic technique for this engineering problem due to some drawbacks…”
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Genetic algorithms for urban transit routing problems by Chew, Joanne Suk Chun

“…Thus in this study, one such metaheuristic algorithm, genetic algorithm (GA) is developed to solve the UTRP. …”
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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

“…Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. …”
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Development of Genetic Algorithm Procedure for Sequencing Problem in Mixed-Model Assembly Lines by Noroziroshan, Alireza

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The new efficient and accurate attribute-oriented clustering algorithms for categorical data by Qin, Hongwu

“…Four real-life data sets obtained from University of California Irvine (UCI) machine learning repository and ten synthetically generated data sets are used to evaluate MGR and IG-ANMI algorithms, and other four algorithms are used to compare with these two algorithms. …”
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Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm by Golshan, Abolfazl

“…Finally, non-dominated sorting genetic algorithm-II as evolutionary optimization approach was used for multi-objective optimization of the micro-end milling process. …”
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Tree-based contrast subspace mining method by Florence Sia Fui Sze

“…The research works involve first preparing the real world numerical and categorical data sets. Then, the tree-based method, the genetic algorithm based parameter values identification of tree-based method, and followed by the genetic algorithm based tree-based method, for numerical data sets are developed and evaluated. …”
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OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”

Faculty timetabling using genetic algorithm by Liong, Boon Yaun

“…Faculty Timetabling using Genetic Algorithm (FTGA) is an application that generate optimum timetable for faculty.The target user of this application is faculty staff who responsible in generate timetable.The problem statement of the project is many clashing exist in the timetable.Faculty staff needs to solve the clashing manually.This will waste time and it is a problem for staff to solve the clashing.By implement GA,clashing will be reduced.The objective of the project is to develop aprototype in scheduling application for generates an optimum timetable for a faculty.Genetic algorithm will be implemented.The scope of FTGA is Faculty of Computer Systems & Software Engineering (FCSSE).The methodology use in this project is prototype model.The testing result show 95 out of 100 test cases achieved the maximum fitness value which means there is no clashing in the timetable.The maximum generation is set to 15 generation.Population for each generation is 3 populations.Percentage of FTGA solve the problem is 95%.…”
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