Search Results - (( course optimization based algorithm ) OR ( java application optimized algorithm ))
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Smart student timetable planner
Published 2025“…A Genetic Algorithm forms the core scheduling engine, generating optimized timetables that respect both hard constraints, such as avoiding clashes, and soft constraints, such as personal preferences.The final output of this project is a functional web-based timetable planner that successfully enhances scheduling efficiency, reduces the likelihood of errors, and improves the overall academic planning experience. …”
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
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Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid
Published 2017“…Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. …”
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Thesis -
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Route Optimization System
Published 2005“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Final Year Project -
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A harmony search algorithm for university course timetabli
Published 2012“…The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. …”
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Article -
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Harmony great deluge for solving curriculum based course timetabling problem
Published 2013“…University course timetabling which has been determined as non deterministic polynomial problem that accept widely as problem that are intractable.An efficient algorithm does not exist that is guaranteed to find an optimal solution for such problems.The design of good algorithm to find new methods and techniques to solve such problem is a very active area of research.This paper presents the adaption of the hybridizing between harmony search with great deluge algorithm for solving curriculum-based course timetabling problems.The algorithm can be adapted to the problem.Results were not comparatively better than those previously known as best solution.Proper modification in terms of the approach in this algorithm would make the algorithm perform better on curriculum-based course timetabling.…”
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Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil
Published 2024“…The proposed approach utilizes genetic algorithms to dynamically produce optimized timetables based on individual student needs, with real-time data scraping from 'iCRESS' ensuring the system stays up to date with the latest course information for accurate timetable generation. …”
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Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid
Published 2017“…Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. …”
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Book Section -
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Performance comparison of linear and non-linear great deluge algorithms in solving university course timetabling problems
Published 2017“…The reason may be due to the constraints involved varied for each dataset as the optimization problems solved by any algorithms are uniformed in relation to the NFL theorems.…”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
Published 2021“…We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. …”
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Investigating a round robin strategy over multi algorithms in optimising the quality of university course timetables
Published 2023“…The performance of the approach is tested with over two sets of benchmark datasets, that is, enrolment-based course timetabling and curriculum-based course timetabling (UD1) in comparison with a set of state-of-the-art methods from the literature. …”
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Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction
Published 2025“…Genetic algorithms for hyperparameter optimization significantly contributed, with the GA + LSTM + ADAGRAD model achieving 88% and 87% accuracy in the 7th and 9th models for BBB course data. …”
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Multiple case-based retrieval for university course timetabling problem
Published 2016“…The case-based retrieval technique is further enhanced and improvised into a new algorithm known as Multiple Case-based Retrieval. …”
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Thesis -
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Ant colony optimization algorithm for load balancing in grid computing
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Monograph -
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Adapting And Hybridising Harmony Search With Metaheuristic Components For University Course Timetabling
Published 2010“…Harmony Search Algorithm (HSA) is a recent metaheuristic population-based method. …”
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Thesis -
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Design and statistical analysis of initial solution construction approach in curriculum based course timetabling problem
Published 2017“…This paper investigates the construction phase approach in which the sequential order of the courses/lectures applied several combination of graph heuristics to generate a population of initial solutions in curriculum-based course timetabling (CBCTT).The construction of population of initial solution is a prerequisite in a population-based metaheuristic implementation. …”
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