Using Genetic Algorithms to Simulate Evolution

Tracking the progress using metrics

Up to this chapter, we’ve spent all our time learning about the details and intricacies that drive genetic algorithms. We 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 we’ve solved has been to optimize an objective. In all of the algorithms we’ve written, we define the problem, configure the algorithm, and run the algorithm until we obtain a solution. While, for the most part, the process of obtaining the solution is the most important thing, sometimes we need a way to track the progress of an evolution over time.