Evolutionary Computation Lab

Case study based experimentation to be followed for all the experiments.

1. Deep Neural Network Architecture Search: Discover optimal architectures for deep neural networks, improving their performance on tasks like image recognition and natural language processing.
2. Swarm Robotics: To optimize the collective behaviour of swarms of robots, enabling them to coordinate and perform tasks efficiently, such as cooperative transport or exploration missions.
3. Automated Machine Learning: Study the process of machine learning, including feature selection, hyper-parameter tuning, and model selection, making it easier for non-experts to apply machine learning algorithms effectively.
4. Energy Management in Smart Grids: Study to optimize energy management in smart grids, facilitating demand-response scheduling, load balancing, and renewable energy integration.
5. Drug Discovery: To study and analyse molecules for drug discovery, accelerating the identification of potential candidates with desired properties and reducing the time and cost of the development process.
6. Cybersecurity: To optimize intrusion detection systems, network security protocols, and malware detection algorithms, enhancing the ability to detect and respond to cyber threats.
7. Multi-Objective Optimization: To solve multi-objective optimization problems in various domains, including, resource allocation, and decision-making.
8. Traffic Signal Control: Study to optimize traffic signal timings and control strategies, improving traffic flow, reducing congestion, and minimizing travel time in urban areas.
9. Renewable Energy System Design: Understand and study to optimize the design and placement of renewable energy systems, such as solar panels and wind turbines, maximizing energy generation and minimizing costs.
10. Supply Chain Optimization: Study to optimize supply chain networks, including inventory management, distribution routing, and supplier selection, improving efficiency and reducing costs.