Applications of Genetic Algorithms

This is going to be a series on applications of Genetic Algorithms. The first will focus on Combinatorial Optimization applications. Next up will be applications for:

• General Optimization Problems
• Machine Learning
• Evolutionary Robotics
• Image and Signal Processing
• Creative Applications
• Economic Modeling

Combinatorial Optimization

The canonical example that we've all hear of is the Traveling Salesman Problem. This applies broadly to many logistics and supply chain problems. Ever wonder how Delta and American Airlines optimize the hub network?
This is a classic application for genetic algorithms.

Every wonder how one might manage a fleet of Uber cars? Besides having someone looking at a map, literally, and smashing the surge pricing in an area that needs more cars, you could also apply genetic algorithms to this problem.

Deciding which jobs to allocate to which people. Or how to schedule the right people at the right time. Hospital operating room scheduling along with the right surgeon and correct set of support staff and supplies are well suited to genetic algorithm optimization. Especially went combined with unstructured data inputs using LLMs.

Distribution and flow problems are natural cases for genetic algorithms.

In general consider genetic algorithms when you are solving these kinds of problems:

• Routing Problems
• Scheduling Problems With Shifts
• Scheduling Problems With Unique Talent Required
• Work Assignment Problems
• Packing Things Into Finite Spaces
• Problems That Are Decomposable Into Graphs