Where to Go Next

As you continue your journey with genetic algorithms, you’ll inevitably need to seek out more advanced resources related to the theory and application of genetic algorithms. Genetic Algorithms in Search, Optimization, and Machine Learning [Gol89] by David Goldberg, while published in 1989, offers some excellent insights into the theory and practice of genetic algorithms. You’ll also find numerous other books on evolutionary computing and evolutionary algorithms in general.

The field of evolutionary algorithms is large. A logical next step would be to research the more nuanced differences between genetic programming, evolution strategies, and genetic algorithms. You might also want to learn more about other algorithms inspired by nature. Many people believe that algorithms derived from nature, such as particle swarm optimization, ant colony optimization, and so on, will have a significant role in the advancements of computing over the next few decades. The Springer Natural Computing Series^[38] offers a number of textbooks on the theory behind evolutionary algorithms and natural computing.

If you want to continue developing genetic algorithms in Elixir and need something a bit more mature than the framework you designed in this book, you can check out Genex,^[39] a framework for writing genetic algorithms in Elixir. The implementation of problems in Genex is nearly identical to how you implemented problems in this book, and it contains a number of other useful tools for the development of genetic algorithms.

Overall, this book was designed to be a stepping stone into the world of genetic algorithms for Elixir programmers. You might not choose to continue working with genetic algorithms, but hopefully you learned a thing or two that you otherwise may not have been exposed to in a traditional Elixir book.