Chapter 2 Breaking Down Genetic Algorithms

Genetic Algorithms in Elixir — by Sean Moriarity (19 / 101)

👈 What You Learned | TOC | Reviewing Genetic Algorithms 👉

In the previous chapter, you learned about informed search and why it’s superior to brute-force search. You were introduced to genetic algorithms and saw how they balance exploitation and exploration for different problems. You used this knowledge to tackle the One-Max problem, which is an introductory optimization problem.

While your previous solution to the One-Max problem was effective, it’s difficult to both tweak and expand. More advanced applications of genetic algorithms will require extensive fine-tuning and experimentation to achieve the best results, which means you need to create modular and easily customizable solutions.

In this chapter, you’ll once again attack the One-Max problem; but your goal this time around is to use the One-Max problem to help you design and build a framework you can use to create genetic algorithms. You can then apply this framework and structure to other problems — making it easier to tweak the different aspects of your algorithms.

👈 What You Learned | TOC | Reviewing Genetic Algorithms 👉

Genetic Algorithms in Elixir by Sean Moriarity can be purchased in other book formats directly from the Pragmatic Programmers. If you notice a code error or formatting mistake, please let us know here so that we can fix it.