Search Results - ((rsolution OR solutions) OR (evolution OR solution)) programming

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  1. 1
    Economic power dispatch solution with non-smooth cost functions using differential evolution: article / Muhammad Firdaus Abd Rahim

    Economic power dispatch solution with non-smooth cost functions using differential evolution: article / Muhammad Firdaus Abd Rahim by Abd Rahim, Muhammad Firdaus

    Published 2011
    “…This thesis proposes a solution for Economic Dispatch problems with non-smooth cost functions using Differential Evolution (DE) algorithm. …”
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  2. 2
    Economic power dispatch solution with non-smooth cost functions using differential evolution / Muhammad Firdaus Abd Rahim

    Economic power dispatch solution with non-smooth cost functions using differential evolution / Muhammad Firdaus Abd Rahim by Abd Rahim, Muhammad Firdaus

    Published 2011
    “…This thesis proposes a solution for Economic Dispatch problems with non-smooth cost functions using Differential Evolution (DE) algorithm. …”
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  3. 3
    Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus

    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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  4. 4
    Minimization of power loss by evolutionary programming using Thyristor Controlled Series Compensators (TCSC) / Fazleza Abdul Latiff

    Minimization of power loss by evolutionary programming using Thyristor Controlled Series Compensators (TCSC) / Fazleza Abdul Latiff by Abdul Latiff, Fazleza

    Published 2007
    “…The evolution of solution is carried out through mutation and competitive selection. …”
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  5. 5
    Development of heuristic methods based on genetic algorithm (GA) for solving vehicle routing problem

    Development of heuristic methods based on genetic algorithm (GA) for solving vehicle routing problem by Ismail, Zuhaimy, Nurhadi, Irhamah, Zainuddin, Zaitul Marlizawati

    Published 2008
    “…Genetic Algorithm gives a pool of solutions rather than just one. The process of finding superior solutions mimics the evolution process, with solutions being combined or mutated to find out the pool of solutions. …”
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  6. 6
    Sub-route reversal repair mechanism and differential evolution for urban transit network design problem

    Sub-route reversal repair mechanism and differential evolution for urban transit network design problem by Tarajo, Buba Ahmed

    Published 2017
    “…Due to the NP-hard nature of the UTNDP, the evaluation of candidate solution is challenging and time consuming, in which many potential solutions are discarded on the grounds of infeasibility. …”
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  7. 7
    An international training program in library and information science : looking backward and forward / Paul Nieuwenhuysen

    An international training program in library and information science : looking backward and forward / Paul Nieuwenhuysen by Nieuwenhuysen, Paul

    Published 2011
    “…How to organize the management of the program? How to exploit the fast evolution of ICT, to announce each new program? …”
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  8. 8
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Huong, Audrey, Sathasivam, Saratha, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  9. 9
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  10. 10
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majid Khan bin Majahar Ali, Majid Khan bin Majahar Ali

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  11. 11
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  12. 12
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  13. 13
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  14. 14
    An improved grey wolf with whale algorithm for optimization functions

    An improved grey wolf with whale algorithm for optimization functions by Asgher, Hafiz Maaz

    Published 2022
    “…Since its inception in 2014, GWO is able to successfully solve several optimization problems and has shown better convergence than the Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Evolutionary Programming (EP). …”
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  15. 15
    Modification of species-based differential evolution for multimodal optimization

    Modification of species-based differential evolution for multimodal optimization by Al Idrus, Said Iskandar, Syahputra, Hermawan, Firdaus, Muliawan

    Published 2015
    “…Optimization problem is the problem of maximizing or minimizing a function of one variable or many variables, which include unimodal and multimodal functions. Differential Evolution (DE), is a random search technique using vectors as an alternative solution in the search for the optimum. …”
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  16. 16
    Properties Of Indefinite Matrix Constraints For Linear Programming In Optimal Solution

    Properties Of Indefinite Matrix Constraints For Linear Programming In Optimal Solution by Sam, Mei Lee

    Published 2018
    “…Finding the optimum solution in engineering and science is a common problem where one wishes to get the objective under certain constraints.This situation is also a typical issue in manufacturing industries where maximum profit and minimum cost are a common objective under certain constraints on the available resources.One approach to solve optimization is to use formulation problem in linear form and subjects to linear constraints,the problem can be deliberated as linear programming problem.The linear constraints can be in a form of a matrix.There are limited researches that discuss the effect of the properties of matrix constraint to the solution.In fact,the matrix constraint has significant influence to the existent of the optimal solution to the optimization problem.This research focused on the investigation of characteristics of non-symmetric indefinite square matrices of linear programming problems which represent the constraints of linear programming problems.The non-symmetric indefinite square matrices are generated randomly by the MATLAB simulation software and its indefinite properties are verified through the principal minor test,quadratic form test and eigenvalues test.The solutions of the primal and dual linear programming problem are simulated and discussed.Optimization software,LINGO,is used to validate the solutions to assure the reliability of the simulated solutions in the MATLAB software.Based on the simulation results,some of the non-symmetric indefinite random matrices found duality gap and those matrices could not provide optimal solution to the problem.Whereas,some indefinite matrices with certain characteristics could achieve optimal solution and no duality gap presented.An indefinite random matrix with all positive off-diagonal entries and the determinant of leading principal minors with positive sign at odd orders and negative sign at even orders surely deliver the optimal solution to the linear programming problems.This research may contribute to the advancement of linear programming solution particularly when the constraints form an indefinite matrix.…”
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  17. 17
    Application of genetic algorithm and JFugue in an evolutionary music generator

    Application of genetic algorithm and JFugue in an evolutionary music generator by Tang, Jia Rou

    Published 2025
    “…Music that has been generated using JFugue involves real-time generation and user-driven evolution. This will involve the explanation of the use of evolution algorithms combined with the music programming to be able to create creative digital music.…”
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  18. 18
    Implementing an effective knowledge management program: A best practice case study

    Implementing an effective knowledge management program: A best practice case study by Damodaran, Meledath

    Published 2006
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  19. 19
    Vehicle routing problem: models and solutions

    Vehicle routing problem: models and solutions by Liong, Choong Yeun, Wan Rosmanira Ismail, Khairuddin Omar, Mourad Zirour

    Published 2008
    “…Among the recently applied heuristic techniques are genetic algorithm, evolution strategies and neural networks…”
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  20. 20
    Differential evolution for neural networks learning enhancement

    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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