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  1. 1
    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs

    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

    Published 2019
    “…Therefore, this study proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing to identify the possible execution path extracted from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. …”
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  2. 2
    Multi population traditional Evolutionary Programming approach for Distributed Generation / Mohd Nazmi Adnan

    Multi population traditional Evolutionary Programming approach for Distributed Generation / Mohd Nazmi Adnan by Adnan, Mohd Nazmi

    Published 2009
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  3. 3
    A new meta heuristic evolutionary programming (NMEP) in optimizing economic energy dispatch

    A new meta heuristic evolutionary programming (NMEP) in optimizing economic energy dispatch by Mohamad Ridzuan, Mohamad Radzi, Hassan, Elia Erwani, Abdullah, Abdul Rahim, Bahaman, Nazrulazhar, Abdul Kadir, Aida Fazliana

    Published 2016
    “…The proposed optimization algorithm, namely New Meta-Heuristic Evolutionary Programming (NMEP) algorithm is followed to Meta-Heuristic Evolutionary Programming (Meta-EP) approach with some modification where the cloning process embedded as a significant progress during the implementation. …”
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  4. 4
    Immune multistage evolutionary programming (IMEP) for optimal load shedding approach for loss minimization / Muhammad Faris Baharuddin

    Immune multistage evolutionary programming (IMEP) for optimal load shedding approach for loss minimization / Muhammad Faris Baharuddin by Baharuddin, Muhammad Faris

    Published 2014
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  5. 5
    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer

    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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  6. 6
    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
    “…Results show that this evolutionary approach is very efficient and able to produce good results compared with other approaches e.g. classical search algorithms or linear programming. � 2010 IEEE.…”
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  7. 7
    Solving unit commitment problem with solar photovoltaic and wind energy generation by using multi-agent evolutionary programming technique / Putri Azimah Salleh

    Solving unit commitment problem with solar photovoltaic and wind energy generation by using multi-agent evolutionary programming technique / Putri Azimah Salleh by Salleh, Putri Azimah

    Published 2014
    “…Several Artificial Intelligence (AI) techniques such as Multi Agent and Evolutionary Programming were combine to produce Multi Agent Evolutionary Programming (MAEP) technique. …”
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  8. 8
    Dual-head marking performance optimisation via evolutionary solutions

    Dual-head marking performance optimisation via evolutionary solutions by Koh J., Tiong S.K., Aris I.B., Mahmoud S.

    Published 2023
    “…Also, the performance of the new operators for evolutionary approaches to the time-based problem has been discussed in the paper. � 2005 IEEE.…”
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  9. 9
    Solving Unit Commitment Problem Using Multi-agent Evolutionary Programming Incorporating Priority List

    Solving Unit Commitment Problem Using Multi-agent Evolutionary Programming Incorporating Priority List by Othman M.N.C., Rahman T.K.A., Mokhlis H., Aman M.M.

    Published 2023
    “…This paper presents an approach to solve the unit commitment problem using a newly developed Multi-agent Evolutionary Programming incorporating Priority List optimisation technique (MAEP-PL). …”
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  10. 10
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    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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  11. 11
    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment

    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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  12. 12
    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
    “…One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. 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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  13. 13
    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
    “…One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. 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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  14. 14
    Automated synthesis of mobile game environments and rulesets using a hybridized interactive evolutionary programming approach

    Automated synthesis of mobile game environments and rulesets using a hybridized interactive evolutionary programming approach by Jia, Hui Ong, Teo, Jason Tze Wi

    Published 2014
    “…By hybridizing Evolutionary Programming (EP) with Interactive Evolutionary Algorithm (IEA), game rules and its playing environment will be automatically generated for an arcade-type game that can be played on the Android mobile platform. …”
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  15. 15
    Hybrid evolutionary-dolphin echolocation programming for sizing optimization of stand-alone photovoltaic systems / Zulkifli Othman

    Hybrid evolutionary-dolphin echolocation programming for sizing optimization of stand-alone photovoltaic systems / Zulkifli Othman by Othman, Zulkifli

    Published 2021
    “…Subsequently, the CI-based sizing algorithm, known as Evolutionary-Dolphin Echolocation Programming Sizing Algorithm (EDEPSA) is formulated to determine the optimal models of each system component such that either Performance Ratio (PR) or Levelized Cost of Electricity (LCOE) of the system is optimized. …”
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  16. 16
    Computational intellegence approach for overcurrent relay coordination

    Computational intellegence approach for overcurrent relay coordination by Qamarul Ariff Azlan

    Published 2024
    text::Final Year Project
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  17. 17
    A decomposition/aggregation method for solving electrical power dispatch problems

    A decomposition/aggregation method for solving electrical power dispatch problems by Mansor M.H., Irving M.R., Taylor G.A.

    Published 2023
    “…A program has been developed to demonstrate the algorithm using the MATLAB programming language. …”
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  18. 18
    A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm

    A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm by Kahourzade, S., Mahmoudi, A., Mokhlis, Hazlie

    Published 2015
    “…This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. …”
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  19. 19
    Smart appointment organizer for mobile application / Mohd Syafiq Adam

    Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

    Published 2009
    “…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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  20. 20
    Unit commitment in power system using multi-agent evolutionary programming incorporating priority listing optimisation technique / Muhammad Nazree Che Othman

    Unit commitment in power system using multi-agent evolutionary programming incorporating priority listing optimisation technique / Muhammad Nazree Che Othman by Che Othman, Muhammad Nazree

    Published 2013
    “…This research presents an approach to solve the UC problem using a newly developed Multi-agent Evolutionary Programming incorporating Priority Listing optimisation technique (MAEP-PL). …”
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