A Genetic Algorithm (GA) is a search heuristic inspired by the process of natural selection and genetics. It is used for optimization and search problems and falls under the broader category of evolutionary algorithms. Genetic algorithms are particularly effective for finding approximate solutions to optimization and search problems where the solution space is large and complex.

Here's a brief overview of how Genetic Algorithms work:

1. Initialization:

   • A population of potential solutions (individuals or chromosomes) to the optimization problem is generated randomly.

   
   

2. Evaluation:

   • Each individual in the population is evaluated for its fitness, which represents how well it solves the given problem. The fitness function quantifies the quality of each solution.

   
   

3. Selection:

   • Individuals are selected from the current population based on their fitness. Those with higher fitness have a higher chance of being selected, simulating the "survival of the fittest" principle.

   
   
4. 
   
5. 
   
6. 
   
7.