An evolutionary algorithm for solving the geometrically constrained site layout problem by Zouein, P.

“…This paper presents an investigation of applying an evolutionary approach to optimally solve the aforementioned layout problem. The proposed algorithm is two-phases: an initialization phase that generates an initial population of layouts through a sequence of mutation operations, and a reproduction phase that evolve the layouts generated in phase one through a sequence of genetic operations aiming at finding an optimal layout. …”
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Genetic Algorithm for Solving Site Layout Problem with Unequal-Size and Constrained Facilities by Zouein, P. P.

“…In most problems considered where the total-objects-to-site-area ratio did not exceed 60%, the algorithm returned close to optimal solutions in a reasonable time.…”
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Decomposition method for solving a nonlinear business cycle model by Dibeh, Ghassan

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On-site workshop investment problem: A novel mathematical approach and solution procedure by Nima Moradi (19418821)

“…Two new (linear) mathematical programming models are proposed for MOSWIPT. Next, due to the NP-hardness of the problem, an enhanced Genetic Algorithm (GA)-based metaheuristic with efficient problem-specific improvement rules as local search and effective crossover and mutation operators is proposed. …”

A New Parallel Genetic Algorithm Model by Al-Somani, Turki F.

“…This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. …”
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XBeGene: Scalable XML Documents Generator by Example Based on Real Data by Harazaki, Manami

“…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities by Mahdi Mokhtarzadeh (11593310)

“…A multi-objective mixed-integer non-linear programming (MINLP) model is developed. To solve the proposed model, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA-II (KNSGA-II), and a hybrid of K-medoids and MOPSO (KMOPSO) are implemented. …”

Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

Subjects:

Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”

Parameter Estimation Of Wiener-Hammerstein Models Via Genetic Algorithms by Emara-Shabaik, Husam

“…In this paper, a modified genetic algorithm is used as a procedure to solve the parameter identification problem of the nonlinear Wiener-Hammerstein models. …”
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A genetic-based algorithm for fuzzy unit commitment model by Mantawy, A.H.

“…The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. …”
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Data Embedding in HEVC Video by Modifying the Partitioning of Coding Units by Shanableh, Tamer

“…The data embedding algorithm guarantees that a maximum of one partition is modified per message segment, therefore, in a given CU, either 0, 1 or 2 partitions are modified. …”
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A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems by Raka Jovanovic (17947838)

“…To be able to solve large problem instances corresponding to real-world applications of the model, a metaheuristic approach is investigated. …”

Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk by Mohamed Chaouch (17983846)

“…<p dir="ltr">This paper introduces novel nonparametric supervised learning techniques for classifying massive datasets, addressing key limitations of existing methods in Big and Streaming Data framework. …”

Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm by Ayman Khallel Al-Ani (17541447)

“…Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. …”

A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT by Dhananjay Bisen (19482454)

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore (17727684)

“…The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. …”

Fuzzy genetic algorithm for floorplanning by Youssef, H.

“…Genetic algorithms (GAs) have been found to be very effective in solving numerous optimization problems, especially those with many (possibly) conflicting and noisy objectives. …”
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Correlation Clustering with Overlaps by Fakhereldine, Amin

“…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems by Faria Nawshin (21841598)

“…<p dir="ltr">Federated Learning (FL) is gaining traction in Android-based consumer electronics, enabling collaborative model training across decentralized devices while preserving data privacy. However, the increasing adoption of FL in these devices exposes them to adversarial attacks that can compromise user data and device security. …”