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  1. 1
    CSC126 - Fundamentals of Algorithms and Computer Problem Solving / Faculty of Computer And Mathematical Sciences

    CSC126 - Fundamentals of Algorithms and Computer Problem Solving / Faculty of Computer And Mathematical Sciences by UiTM, Faculty of Computer And Mathematical Sciences

    Published 2023
    “…This course is an introduction to problem solving using a computer programming language. …”
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  2. 2
    Optimal power flow based on fuzzy linear programming and modified Jaya algorithms

    Optimal power flow based on fuzzy linear programming and modified Jaya algorithms by Alzihaymee, Warid Sayel Warid

    Published 2017
    “…The first proposed approach is a multi-objective fuzzy linear programming optimization (MFLP) algorithm to solve the MOOPF problem. …”
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  3. 3
    Algorithm As A Problem Solving Technique For Teaching And Learning Of The Malay Language

    Algorithm As A Problem Solving Technique For Teaching And Learning Of The Malay Language by Jano, Zanariah, Omar, Norliza, Nazir, Faridah

    Published 2019
    “…This implies that the algorithm concept of how to prepare a cake through a scratch program has been fully understood by the students. …”
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  4. 4
    Algorithm as a problem solving technique for teaching and learning of the Malay language

    Algorithm as a problem solving technique for teaching and learning of the Malay language by Nazir, Faridah, Jano, Zanariah, Omar, Norliza

    Published 2019
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    Proceeding Paper
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  5. 5
    Algorithm as a problem solving technique for teaching and learning of the Malay language

    Algorithm as a problem solving technique for teaching and learning of the Malay language by Faridah Nazir, Zanariah Jano, Norliza Omar

    Published 2019
    “…This implies that the algorithm concept of how to prepare a cake through a scratch program has been fully understood by the students. …”
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  6. 6
    Comparison Between Linear Programming And Integer Linear Programming: A Review

    Comparison Between Linear Programming And Integer Linear Programming: A Review by Sam, Mei Lee, Saptari, Adi, Salleh, Mohd Rizal, Mohamad, Effendi

    Published 2018
    “…Exact algorithms are suitable for small sized problems and attain optimal solution in reasonable computational time. …”
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  7. 7
    Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem

    Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem by Ngo, T.S., Jaafar, J., Aziz, I.A., Aftab, M.U., Nguyen, H.G., Bui, N.A.

    Published 2022
    “…We designed a hybrid version of the genetic algorithm with the local search algorithm to solve the proposed problem. …”
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  8. 8
    Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance

    Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance by Nor Azlan, Nor Asmaa Alyaa

    Published 2021
    “…Linear programming (LP) is a mathematical modelling that formulate a problem into three components which are decision variables, objective function and constraints. …”
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  9. 9
    Using genetic algorithm for solving N-Queens problem

    Using genetic algorithm for solving N-Queens problem by Turky A.M., Ahmad M.S.

    Published 2023
    “…The N-Queens problem is a well-known NP-Hard problem. Optimal solutions to small N values can be found in reasonable time by classical search algorithms or linear programming. …”
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  10. 10
    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO)

    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO) by Mohd Erwan Mazalan

    Published 2022
    “…This project present a system program algorithm based on Moth Flame Optimization (MFO) algorithm to assign an optimal set of system program to meet the satisfaction of quality of service requirements of cloud computing in such a way that the total execution time of tasks is minimized. …”
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  11. 11
    A comparative study of greedy algorithms and dynamic programming in road network / Nuralya Sofea Hairulanuar

    A comparative study of greedy algorithms and dynamic programming in road network / Nuralya Sofea Hairulanuar by Hairulanuar, Nuralya Sofea

    Published 2024
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  12. 12
    CSC752: Advanced Algorithm & Analysis / College of Computing, Informatics and Media

    CSC752: Advanced Algorithm & Analysis / College of Computing, Informatics and Media by UiTM, College of Computing, Informatics and Media

    Published 2022
    “…Algorithmic problems form the core of computer science, and thus algorithm design and analysis is among its most fundamental elements. …”
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  13. 13
    CSC752: Advanced Algorithm & Analysis / College of Computing, Informatics and Mathematics

    CSC752: Advanced Algorithm & Analysis / College of Computing, Informatics and Mathematics by UiTM, College of Computing, Informatics and Mathematics

    Published 2018
    “…Algorithmic problems form the core of computer science, and thus algorithm design and analysis is among its most fundamental elements. …”
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  14. 14
    Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja

    Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja by Nurul Hani , Nortaja

    Published 2004
    “…Nonetheless, the A • algorithm do have some unique properties which is more efficient in finding the shortest path especially in human real-world problem. …”
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  15. 15
    Enhancement of Ant System Algorithm for Course Timetabling Problem

    Enhancement of Ant System Algorithm for Course Timetabling Problem by Djamarus, Djasli

    Published 2009
    “…As a member of the NP Problem, an exact algorithm to solve the course scheduling problem is not available to date. …”
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  16. 16
    Some metaheuristic algorithms for solving multiple cross-functional team selection problems

    Some metaheuristic algorithms for solving multiple cross-functional team selection problems by Ngo, S.T., Jaafar, J., Izzatdin, A.A., Tong, G.T., Bui, A.N.

    Published 2022
    “…We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. …”
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  17. 17
    Overview of metaheuristic: classification of population and trajectory

    Overview of metaheuristic: classification of population and trajectory by Zainul Rashid, Zarina

    Published 2010
    “…Algorithms are used to find the solutions through the computer program. …”
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    Monograph
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  18. 18
    A Modified Giffler and Thompson Genetic Algorithm on the Job Shop Scheduling Problem

    A Modified Giffler and Thompson Genetic Algorithm on the Job Shop Scheduling Problem by Lee, Hui Peng, Salim, Sutinah

    Published 2006
    “…This algorithm is modified to produce better results than the existing algorithm by using Visual Prolog programming language.…”
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  19. 19
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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  20. 20
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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