Developing a Genetic Algorithm to solve problems
When we look at the world around us, we sometimes wonder how everything we see and interact with came to be. One way to explain this is the theory of evolution. In this session we will discuss about what are evolution techniques, what is genetic algorithm(GA), and its life-cycle.
We will see how GA are used to evaluate large search spaces for a good solution. It is important to note that a genetic algorithm is not guaranteed to find the absolute best solution. But we will see by solving a problem, that it can attempt to find the global best whilst avoiding local best solutions.We will demystify all the terms which are used in GA, like genes, populations, chromosome, fitness function, mutation, cross-over by solving a problem. And lastly we will see its importance in Reinforcement Learning in today's world.
Outline/Structure of the Demonstration
1. Presentation for 5 mins on GA and its life cycle
2. Live code - solving a board colour problem - 15 mins
- AI Search Techniques
- Introduction to Genetic Algorithms(GA)
- Importance of GA in Reinforcement Learning
Software Developers, AI/ML Practitioners, Anyone interested to know about AI Search Algorithms
Prerequisites for Attendees
Basics of python
Basic understanding of Search Techniques