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  1. 1

    Solving single machine scheduling problem with maximum lateness using a genetic algorithm by Nazif, Habibeh, Lee, Lai Soon

    Published 2010
    “…We develop an optimised crossover operator designed by an undirected bipartite graph within a genetic algorithm for solving a single machine family scheduling problem, where jobs are partitioned into families and setup time is required between these families. …”
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  2. 2

    Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources by Abed, Munther Hameed, Mohd Nizam Mohmad, Kahar

    Published 2022
    “…Here, we proposed genetic algorithm (GA) to solve the UPMR problem because of the robustness and the success of GA in solving many optimization problems. …”
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  3. 3

    Genetic-local hybrid optimizer for solving advance layout problem by Taha, Imad, Ibrahim Habra, Hind Saleem

    Published 2006
    “…Results show the potentiality of the proposed algorithm in solving the problem and outperforming previous algorithms.…”
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  4. 4

    The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement by Hosseini E., Al-Ghaili A.M., Kadir D.H., Daneshfar F., Gunasekaran S.S., Deveci M.

    Published 2025
    “…In the past few decades, there have been multiple algorithms proposed for the purpose of solving optimization problems including Machine Learning (ML) applications. …”
    Article
  5. 5

    A combinatorial optimization technique using genetic algorithm :a case study in machine layout problem by Lau, Yung Siew.

    Published 2007
    “…Genetic Algorithms (GAs) are a powerful stochastic search in solving optimization problems. …”
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    Final Year Project Report / IMRAD
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    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

    Published 2017
    “…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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    Thesis
  8. 8

    Development of cell formation algorithm and model for cellular manufacturing system by Nouri, Hossein

    Published 2011
    “…The results show proposed algorithm approximately solved problems averagely 22% better in terms of find feasible optimal solutions depends of various performance measures in 72.2% of computational time than other previous considered key algorithms.…”
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    Thesis
  9. 9

    Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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  10. 10

    An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective by Mousavi, Seyed Mohsen, Sadeghi R., Kiarash, Lee, Lai Soon

    Published 2023
    “…To integrate machine learning and human interactions, this paper develops a new three-stage interactive algorithm in business analytics, called the interactive Nautilus-based algorithm, to address complex problems. …”
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  11. 11

    Development of committee machine models for multiple response optimization problems by Golestaneh, Seyed Jafar

    Published 2014
    “…Four methodologies are to make four different CM models to solve MRO problems. The fifth methodology proposes the final algorithm which uses four CM models together to solve MRO problems. …”
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    Thesis
  12. 12

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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    Thesis
  13. 13

    Higher desirability in solving multiple response optimization problems with committee machine by Golestaneh, Seyed Jafar, Ismail, Napsiah, Mohd Ariffin, Mohd Khairol Anuar, Tang, Sai Hong, Naeini, Hassan Moslemi, Maghsoudi, Ali Asghar, Firoozi, Zahra

    Published 2014
    “…Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) in combination with genetic algorithm (GA) is applied for modeling and optimization of MRO problems. …”
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  14. 14

    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment by Shaliza Hayati A. Wahab, Nordin Saad, Azali Saudi, Ali Chekima

    Published 2021
    “…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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  15. 15

    Particle swarm optimization (PSO) for CNC route problem by Nur Azia Azwani, Ismail

    Published 2002
    “…The purpose of this study is to develop the application of Particle Swarm Optimization (PSO) which applicable to CNC machine route problem. In this project, the problem of CNC machine can be identifying by routing problem which it become more complicated to solve without use of any optimization method. …”
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    Undergraduates Project Papers
  16. 16

    Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying by Liu , Zongying

    Published 2019
    “…Besides, concept drift problem in on-line learning model is solved by Drift Detection Machine (DDM). …”
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    Thesis
  17. 17

    Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

    Published 2023
    “…Secondly, in solving every Machine Learning problem, there is no one algorithm superior to other algorithms. …”
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    Book Section
  18. 18

    Review on bio-inspired algorithms approach to solve assembly line balancing problem by Noorazliza, Sulaiman, Junita, Mohamad Saleh, Nor Rokiah Hanum, Md. Haron, Z. A., Kamaruzzaman

    Published 2019
    “…Bio-inspired algorithms that have been developed by mimicking the biological phenomenon of nature have been widely applied to solve many real-world problems. …”
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    Conference or Workshop Item
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    Solving Support Vector Machine Model Selection Problem Using Continuous Ant Colony Optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Ant Colony Optimization has been used to solve Support Vector Machine model selection problem.Ant Colony Optimization originally deals with discrete optimization problem.In applying Ant Colony Optimization for optimizing Support Vector Machine parameters which are continuous variables, there is a need to discretize the continuously value into discrete value.This discretize process would result in loss of some information and hence affect the classification accuracy and seeking time.This study proposes an algorithm that can optimize Support Vector Machine parameters using Continuous Ant Colony Optimization without the need to discretize continuous value for Support Vector Machine parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed hybrid algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.…”
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