Chapter 9 Tracking Genetic Algorithms

Genetic Algorithms in Elixir — by Sean Moriarity (70 / 101)

👈 What You Learned | TOC | Using Genetic Algorithms to Simulate Evolutio n 👉

Up to this chapter, you’ve spent all your time learning about the details and intricacies that drive genetic algorithms. You learned how to represent solutions, how to evaluate solutions, and how to alter populations using selection, crossover, mutation, and reinsertion.

The goal of all of the problems you’ve solved has been to optimize an objective. In all of the algorithms you’ve written, you define the problem, configure the algorithm, and run the algorithm until you obtain a solution. While, for the most part, the process of obtaining the solution is the most important thing, sometimes you need a way to track the progress of an evolution over time.

Imagine if you wanted to analyze how your population’s collective fitness grew over time. Or perhaps you want to visualize how the distribution of fitness changed between generations. Or even still, perhaps you want to trace the genealogy of your best solution when the algorithm returns.

Metrics are important because they offer insights that can help you make decisions about how to reconfigure or adjust your algorithm. It’s difficult to make decisions and identify bottlenecks in your algorithm without detailed metrics to analyze.

In this chapter, you’ll learn how to integrate utilities that allow you to track various metrics in your framework. You’ll build these utilities around a unique application of genetic algorithms.

👈 What You Learned | TOC | Using Genetic Algorithms to Simulate Evolutio n 👉

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