Скачать или смотреть Day 4: Population-Based Metaheuristics: Genetic Algorithms, Ant Colony Optimization with Python Code

Welcome to the final session of our workshop, "Metaheuristics: A Class of Intelligent Search Methods in AI"! In this lecture, we explore the powerful world of Population-Based Metaheuristics, focusing on Evolutionary Algorithms and Swarm Intelligence.

In this session, we explore population-based metaheuristics, evolutionary algorithms, and swarm intelligence, covering key concepts and their applications across various domains.
This session covers the fundamental theory behind Genetic Algorithms (GA) and Ant Colony Optimization (ACO), breaking down how these nature-inspired methods solve complex problems like the Traveling Salesman Problem (TSP). We conclude with a hands-on Python coding session where we implement a Genetic Algorithm for the classic Knapsack Problem.

🔗 Workshop Materials & Resources:
Access all slides, code, and supplementary materials here:
https://sites.google.com/view/shubham...
Other Resources: https://sites.google.com/view/shubham...

⏱️ TIMESTAMPS / CHAPTERS:
0:00:00 - Introduction & Workshop Recap
0:04:07 - Real-World Applications of Optimization
0:07:28 - Population-Based Metaheuristics: Theory & Concepts
0:37:39 - Genetic Algorithms (GA): Selection, Crossover & Mutation
0:54:40 - Swarm Intelligence & Ant Colony Optimization (ACO)
1:48:40 - Hands-On Python: Implementing a Genetic Algorithm for the Knapsack Problem

🔹 Key Metaheuristic Methods Discussed:
Genetic Algorithms (GA): We explore the core principles of selection (survival of the fittest), crossover (reproduction), and mutation to evolve a population of solutions over generations.
Ant Colony Optimization (ACO): Learn how the collective foraging behavior of ants, using pheromone trails, can be modeled to find optimal paths in problems like the TSP.
Swarm Intelligence: A broader look at how decentralized, self-organized systems in nature inspire powerful optimization techniques.

Topics Covered:
🔹 Applications of Optimization – How real-world problems can be framed as optimization problems.
🔹 Population-Based Metaheuristics – Understanding S-metaheuristics vs. P-metaheuristics.
🔹 Evolutionary Algorithms – Overview, working principles, and key components.
🔹 Genetic Algorithms (GA) – Step-by-step breakdown of selection, crossover, and mutation, including an example of solving the TSP.
🔹 Swarm Intelligence – How the collective behavior of biological species inspires optimization techniques.
🔹 Ant Colony Optimization (ACO) – The concept of pheromone trails and solving the TSP using ACO.
🔹 Coding Hands-On – Implementing Genetic Algorithms (GA) for the knapsack problem using Python.

🔹 Target Audience:
This workshop is designed for students, faculty, and industry professionals interested in AI, optimization, and intelligent search techniques. It's ideal for:
✅ Students & Researchers in AI, Computer Science, and Engineering.
✅ Anyone working with complex combinatorial and continuous optimization problems.
✅ Beginners and professionals looking for a clear explanation of metaheuristics with practical coding examples.

🔹 Workshop Context & Affiliations:
This online workshop (6th – 9th February 2025) is proudly organized by the Department of Electronics and Communication Engineering, NIT Rourkela, in collaboration with the IEEE Student Chapter, Rourkela Section.

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