Back to Work & Research
Research
completed
2024
Genetic Algorithm for Maximum Network Flow
AI-driven optimizer with interactive flow simulation
Course research project exploring evolutionary optimization for network flow. Built a visual simulator and enhanced genetic operators for faster, more stable convergence on directed graphs.
AI/ML
Optimization
Team Lead
Developer
Timeline
2024
Type
Research
Status
completed
Outcome / Impact
- •Interactive simulator to visualize flow networks and GA iterations
- •Adaptive mutation and population seeding improved convergence stability
Tech / Skills
Python
PyQt5
Genetic Algorithm
Graph Theory
Case Study
1) Context / Problem
What problem were you solving? Why does it matter?
2) Your Role
Responsibilities, decisions you owned, stakeholders.
3) Approach
Method, experiments, architecture, or research design.
4) Result / Impact
Metrics, rankings, adoption, business outcome.
5) Learnings
What you’d repeat or change next time.
6) Links
See links above.