Search Results - (( solution population study algorithm ) OR ( java application a algorithm ))

Search alternatives:

Refine Results
  1. 1
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  2. 2
    Examination timetabling using genetic algorithm case study: KUiTTHO

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…These aspects are important for the examination can be done in a smooth way and no students can sit more than one exam in a same time slot. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  3. 3
    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…These aspects are important for the examination can be done in a smooth way and no students can sit more than one exam in a same time slot. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  4. 4
    Design and statistical analysis of initial solution construction approach in curriculum based course timetabling problem

    Design and statistical analysis of initial solution construction approach in curriculum based course timetabling problem by Wahid, Juliana, Mohd Hussin, Naimah

    Published 2017
    “…To produce a population of initial solution require algorithm that can produce multiple feasible solutions and these solutions must be diverse. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  5. 5
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  6. 6
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The key is the trigger mechanism to the algorithm. Until the advent of the Internet, encryption was rarely used by the public, but was largely a military tool. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  7. 7
    The impact of population size on knowledge acquisition in genetic algorithms paradigm: Finding solutions in the game of Sudoku

    The impact of population size on knowledge acquisition in genetic algorithms paradigm: Finding solutions in the game of Sudoku by Abu Bakar, Nordin, Mahadzir, Muhammad Fadhil

    Published 2010
    “…Population size is an important component in genetic algorithms (GAs).The concept of population in GAs has contributed to a unique searching strategy which empower its search process through the massive volume of the data in a population.The purpose of this study is to see how the impact of population size on genetic algorithms in producing correct solution for a Sudoku puzzle.Sudoku is a Japanese number puzzle game that has become a worldwide phenomenon.The puzzle involves completing a grid of cells by assigning a single number to each cell.The numbers in a row or a column must consist of any one of the numbers from 1 to 9; no repetition is allowed.GA will be used to generate the correct solution of Sudoku puzzles.The mechanism to produce legal Sudoku grid will follow the requirements needed and meet all the constraints.A fitness function is designed to evaluate legal grids and GA will be tested for performance and time efficiency.The challenges lie on how GA will represent a Sudoku grid in the process and the effectiveness of its operators such as crossover and mutation.The results show how different population size can produce different solutions.The best performance is observed at 500 population size.The paper will conclude with an insight of this value and its significance to the knowledge acquisition in GA paradigm..…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  8. 8
    Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal

    Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal by Mohd Kamal, Mohamad Fadhil

    Published 2010
    “…The study conducts comparative analyses on the performances of the elitist genetic algorithms (EGA). …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  9. 9
    Provider independent cryptographic tools

    Provider independent cryptographic tools by Ibrahim, Subariah, Salleh, Mazleena, Abdul Aziz, Shah Rizan

    Published 2003
    Get full text
    Get full text
    Get full text
    Monograph
    Save to List
    Saved in:
  10. 10
    A new variant of black hole algorithm based on multi population and levy flight for clustering problem

    A new variant of black hole algorithm based on multi population and levy flight for clustering problem by Haneen Abdul Wahab, Abdul Raheem

    Published 2020
    “…The second modification is the multiple population BH that is proposed as a generalization to the BH algorithm, in which the algorithm was not reliant upon the best solution but rather on a set of best solutions generated, called “MBH”. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  11. 11
    Iteration strategy and ts effect towards the performance of population based metaheuristics

    Iteration strategy and ts effect towards the performance of population based metaheuristics by Nor Azlina, Ab. Aziz, Nor Hidayati, Abdul Aziz, Azlan, Abd Aziz, Tasiransurini, Abdul Rahman, Wan Zakiah, Wan Ismail, Zuwairie, Ibrahim

    Published 2020
    “…The algorithms can be categorized based on number of agents, either single agent algorithms which are also known as single solution metaheuristics or multi agents algorithms, also known as population-based metaheuristics. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  12. 12
    Clustering ensemble learning method based on incremental genetic algorithms

    Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

    Published 2012
    “…Firstly, an architecture for the clustering ensemble based on incremental genetic-based algorithms is proposed consisting of two phases: (i) to produce cluster partitions as initial populations, (ii) to combine cluster partitions and to generate final clustering solution by incremental genetic based clustering ensemble learning algorithm. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  13. 13
    Simulated Kalman Filter algorithms for solving optimization problems

    Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

    Published 2019
    “…However, the HKA algorithm has its own flaws. Although it was introduced as a population-based stochastic optimization algorithm, HKA is not exactly a population-based algorithm because it initializes and updates only a single solution. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  14. 14
    A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre

    A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre by Nurika, O., Hassan, M.F., Zakaria, N., Jung, L.T.

    Published 2018
    “…Study of fluctuation in genetic algorithm has been a sub-objective in genetic algorithm implementations. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  15. 15
    A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre

    A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre by Nurika, O., Hassan, M.F., Zakaria, N., Jung, L.T.

    Published 2018
    “…Study of fluctuation in genetic algorithm has been a sub-objective in genetic algorithm implementations. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  16. 16
    Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line

    Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line by Noroziroshan, Alireza, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Napsiah

    Published 2010
    “…A heuristic algorithm was introduced to generate the initial population for intelligence based genetic algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  17. 17
    Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid

    Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid by Wahid, Juliana

    Published 2017
    “…Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  18. 18
    Comparison Between Genetic Algorithm and Electromagnetism-Like Algorithm for Solving Inverse Kinematics

    Comparison Between Genetic Algorithm and Electromagnetism-Like Algorithm for Solving Inverse Kinematics by Yap, David F. W.

    Published 2012
    “…Different target points have been taken to check the performance of each algorithm to solve the IK problem. The results showed that EM algorithm needs less population size and number of generations to get the true solution. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  19. 19
    An adaptively switching iteration strategy for population based metaheuristics / Nor Azlina Ab. Aziz

    An adaptively switching iteration strategy for population based metaheuristics / Nor Azlina Ab. Aziz by Nor Azlina, Ab. Aziz

    Published 2017
    “…Experiments conducted using three parent algorithms namely particle swarm optimization (PSO), which is a popular population-based optimizer with population and individual memories, gravitational search algorithm (GSA), a memoryless young optimizer, and simulated Kalman filter (SKF), a newly introduced optimization algorithm that use population’s memory to guide an agent’s search, show that iteration strategy is an algorithm dependent parameter as well as function dependent. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  20. 20
    A study of fluctuations in genetic algorithm optimized network in data centre

    A study of fluctuations in genetic algorithm optimized network in data centre by Nurika, O., Hassan, M.F., Zakaria, N., Jung, L.T.

    Published 2017
    “…Study of fluctuation in genetic algorithm has been a sub-objective in genetic algorithm implementations. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:

Search Tools: