Genetic Algorithms Role in AI

Recap

Throughout this course, you learned how to use genetic algorithms to solve optimization problems. You discovered the ins and outs of basic genetic algorithms and worked through solving difficult problems with a problem-solving framework. You designed a genetic algorithm framework from start to finish and expanded on this framework with the addition of basic tracking mechanisms and visualizations. You then optimized your framework with tools like ExProf and Benchee, and you learned how to ensure the code you implement within your framework is correct using tools like ExUnit and dialyxir.

At this point, you have a powerful suite of skills and tools that can be built upon or used to solve practically any problem with a genetic algorithm. One thing you might be wondering now is, “Where do I go next?”

In this chapter, you’ll learn about some of the recent advancements in genetic algorithms, and the recent innovations driven by genetic algorithms and evolutionary algorithms. Additionally, you’ll become familiar with some advanced resources that you can continue to use on your journey with genetic algorithms.

Learning with evolution

Artificial intelligence (AI) and machine learning (ML) have dominated the last decade of computing. AI is experiencing its third “boom” with no signs of stopping. It seems that almost every day there’s a new revolutionary innovation in AI or another startup using AI to improve everyday life. The demand for developers who are familiar with and can solve problems using AI/ML will continue to increase over the next decade.