In this chapter, we provide some advice on best practices, tips, and tricks for writing and analyzing neuroevolution algorithms. By the end of this chapter, you will know how to start working with the problem at hand, how to tune the hyperparameters of the neuroevolution algorithm, how to use advanced visualization tools, and what metrics can be used to the analyze the algorithm's performance. Also, you will learn about the best coding practices for Python, which will help you in the implementation of your projects.
In this chapter, we will cover the following topics:
- Starting with problem analysis
- Selecting the optimal search optimization method
- Using advanced visualization tools
- Tuning hyperparameters and knowing what should be tuned
- Understanding which performance metrics to collect
- Python coding tips and tricks
