ArianitHalimi/AIN_25
Apr 2025 – Apr 2025
1 Month active
Languages Used
Python
Technical Skills
Algorithm DesignAlgorithm ImplementationAlgorithm OptimizationCode RefactoringEvolutionary AlgorithmsGenetic Algorithms
Edi
PROFILE
Edi
Muhamed Zahiri developed advanced configuration exploration features for the ArianitHalimi/AIN_25 repository, focusing on scalable optimization techniques. He implemented a Monte Carlo Search system for library configurations, integrating solver-based generation and evaluation directly into the application to enable data-driven decision-making. Leveraging Python and multiprocessing, he introduced a hybrid parallel evolutionary search that accelerates local search and adapts population-based improvements, enhancing both convergence and scalability. Zahiri also addressed stability by refactoring code, fixing an indentation bug, and refining execution paths. His work demonstrates depth in algorithm design, optimization, and parallel computing, laying a robust foundation for future software enhancements.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
2
Lines of code
783
Activity Months1
Your Network
22 peopleSame Organization
@student.uni-pr.edu6
Work History
April 2025
8 Commits • 2 Features
Apr 1, 2025April 2025 (AIN_25): Key features delivered include Monte Carlo Search for library configurations (solver-based generation/evaluation) with integration hooks in the app, enabling data-driven exploration of configurations. Introduced Hybrid Parallel Evolutionary Search, combining evolutionary algorithms with multiprocessing to accelerate local search and implement adaptive, population-based improvements. Major improvements to the GA algorithm with parallelization for better convergence and scalability. Stability work included an indentation fix in Monte Carlo search and a temporary disablement of its main execution path during stabilization, followed by refinements to re-enable and strengthen the approach. Impact: faster and more scalable configuration exploration, enabling quicker data-driven decisions, reduced deployment risk, and a stronger foundation for future optimization. Technologies demonstrated: Python, Monte Carlo methods, evolutionary algorithms, multiprocessing, code refactoring, testing.
8 Commits • 2 Features
Apr 1, 2025April 2025 (AIN_25): Key features delivered include Monte Carlo Search for library configurations (solver-based generation/evaluation) with integration hooks in the app, enabling data-driven exploration of configurations. Introduced Hybrid Parallel Evolutionary Search, combining evolutionary algorithms with multiprocessing to accelerate local search and implement adaptive, population-based improvements. Major improvements to the GA algorithm with parallelization for better convergence and scalability. Stability work included an indentation fix in Monte Carlo search and a temporary disablement of its main execution path during stabilization, followed by refinements to re-enable and strengthen the approach. Impact: faster and more scalable configuration exploration, enabling quicker data-driven decisions, reduced deployment risk, and a stronger foundation for future optimization. Technologies demonstrated: Python, Monte Carlo methods, evolutionary algorithms, multiprocessing, code refactoring, testing.
April 2025
Activity
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Quality Metrics
Correctness77.6%
Maintainability82.6%
Architecture75.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Algorithm DesignAlgorithm ImplementationAlgorithm OptimizationCode RefactoringEvolutionary AlgorithmsGenetic AlgorithmsOptimizationParallel ComputingSearch AlgorithmsSoftware DevelopmentSoftware OptimizationSoftware Refactoring
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