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  1. 1

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

    Published 2017
    “…In the proposed novel QOJaya algorithm, an intelligence strategy, namely, quasi-oppositional based learning (QOBL) is incorporated into the basic Jaya algorithm to enhance its convergence speed and solution optimality. …”
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    Thesis
  2. 2

    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
    “…The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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  3. 3

    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
    “…The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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  4. 4

    Successor selection for Ant Colony Optimization technique algorithm / Muhammad Iskandar Isman by Isman, Muhammad Iskandar

    Published 2017
    “…ACO algorithm is the best solution because it included the optimization technique to optimized the result based on the data criteria needs. …”
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  5. 5

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. …”
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  6. 6

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. …”
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  7. 7

    A multi-depot vehicle routing problem with stochastic road capacity and reduced two-stage stochastic integer linear programming models for rollout algorithm by Anuar, Wadi Khalid, Lee, Lai Soon, Seow, Hsin Vonn, Pickl, Stefan

    Published 2021
    “…The proposed approach is suitable for obtaining a policy constructed dynamically on the go during the rollout algorithm. The rollout algorithm is part of the Approximate Dynamic Programming (ADP) lookahead solution approach for a Markov Decision Processes (MDP) framed Multi-Depot Dynamic Vehicle Routing Problem with Stochastic Road Capacity (MDDVRPSRC). …”
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  8. 8

    An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection by Shing, Chiang Tan, Mohammed Al-Andoli, Mohammed Nasser, Kok, Swee Lim, Pey, Yun Goh, Chee, Peng Lim

    Published 2023
    “…DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm; therefore, their training and optimization procedures suffer from local sub-optimal solutions. …”
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  9. 9

    Applying case reuse and Rule-Based Reasoning (RBR) in object-oriented application framework documentation: Analysis and design by Jani H.M., Lee S.P.

    Published 2023
    “…We believe that with the reuse of past cases for solving new problems, new framework users will be able to dramatically improve their software development performance. The use of rule-based reasoning and genetic algorithms will optimize the case search and case adaptation process. �2008 IEEE.…”
    Conference Paper
  10. 10

    Towards a better feature subset selection approach by Shiba, Omar A. A.

    Published 2010
    “…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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    Algorithm As A Problem Solving Technique For Teaching And Learning Of The Malay Language by Jano, Zanariah, Omar, Norliza, Nazir, Faridah

    Published 2019
    “…Students are also excited about the algorithmic techniques and the scratch program generated. …”
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  14. 14

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

    Published 2019
    “…Students are also excited about the algorithmic techniques and the scratch program generated. …”
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  15. 15

    Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus by Darus, Zamzuhairi

    Published 2003
    “…This project proposed on an investigation on the voltage stability margin identification using evolution programming learning algorithm. A multilayer feed-forward artificial neural network (ANN) with evolution programming learning algorithm for calculation of voltage stability margins (VSM). …”
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  16. 16

    Opposition-based Whale Optimization Algorithm by Alamri, Hammoudeh S., Alsariera, Yazan A., Kamal Z., Zamli

    Published 2018
    “…In order to improve solution accuracy and reliability, this paper proposes a new algorithm based on WOA. The new algorithm called Opposition-based Whale Optimization (OWOA). …”
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  17. 17

    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

    Published 2022
    “…To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. …”
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  18. 18

    Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem by Ismail, Ibrahim, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2015
    “…In this study, rule-based multi-state gravitational search algorithm (RBMSGSA) algorithm is proposed to solve discrete combinatorial optimization problems. …”
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    Conference or Workshop Item
  19. 19

    Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation by Khan, Abdullah

    Published 2022
    “…Finally, a crowding distance and non-dominated-sorting-based multi-objective hybrid firefly & particle swarm optimization (MOHFPSO) algorithm is designed for MOOPF problems. …”
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  20. 20

    An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP by Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Muzaffar Hamzah, Aida Mustapha, Angela Amphawan

    Published 2021
    “…To evaluate the proposed algorithm, the solutions to the TSP problem obtained from the proposed algorithm and swap sequence based PSO are compared in terms of the best solution, mean solution, and time taken to converge to the optimal solution. …”
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