Let’s start with the famous quote by Charles Darwin:
It is not the strongest of the species that survives, nor the most intelligent , but the one most responsive to change.
You must be thinking what has this quote got to do with genetic algorithm? Actually, the entire concept of a genetic algorithm is based on the above line. Genetic algorithms simulate the process of natural selection which means those species who can adapt to changes in their environment are able to survive and reproduce and go to next generation. In simple words, they simulate “survival of the fittest” among individual of consecutive generation for solving a problem. Can we exploit these algorithms to our advantage to build ML pipelines? The answer is yes! This hack session covers genetic algorithms from the ground up and demonstrates how you can use them in an ML pipeline.
Overview of the session:
- What are genetic algorithms and it’s use cases in ML
- How to solve any optimization problem using Genetic Algorithms
- Overview of the python library DEAP (Distributed Evolutionary Algorithms in Python)
- Demonstration showcasing use of genetic algorithms for:
- Feature Selection
- Feature Creation
Key Takeaways:
- Using GA to solve any optimization problem ranging from a simple knapsack problem to Neural Architecture Search (NAS)
- Building end to end fully automated machine learning pipelines (AutoML solutions), including auto feature creation and selection
Check out the below video to know more about the session.
