This document discusses genetic algorithms, which are adaptive heuristic search algorithms inspired by Darwin's theory of natural selection. Genetic algorithms reflect the process of selection of the fittest through techniques like tournament selection, crossover and mutation to generate high quality solutions to optimization problems. The algorithm iterates through selection, crossover and mutation until certain stopping criteria are met, such as reaching a fixed number of generations, achieving a satisfactory fitness level, or convergence. Genetic algorithms have applications in fields like automotive design and robotics to optimize designs and components.

1. Genetic Algorithm
By Shubham Kadam

2. Ever heard of Darwin's
Evolution Theory?
• Survival of the fittest.
• Natural Selection.

3. What is
Genetic
algorithm?
This algorithm reflects the
process of selection of the
fittest.
It belongs to evolutionary
algorithm, is a adaptive heuristic
search algorithm.
Purpose is to generate high
quality solution for optimization
problem.

4. Lifecycle

6. Algorithm

7. Selection
(Tournament Selection
Method)

8. Example
of Fitness
function for
Individual
selection

9. Crossover

10. Mutation

11. When to
Stop?
Reaching a fixed number of
generations.
Getting a satisfactory fitness
level.
Convergence of the algorithm.

12. Applications
• Automotive Design: Genetic Algorithm can return combinations of best
materials and best engineering to provide faster, lighter, more fuel efficient and
safer vehicles for all the things we use vehicles for.

13. Applications
• Robotics: GAs can be programmed to search for a range of optimal designs and
components for each specific use, or to return results for entirely new types of
robots that can perform multiple tasks and have more general application.

14. Thank-You