3.2. Evolution Graph#
The evolution graph visualizes the progression of fitness scores across generations in a genetic optimization process. This plot helps you understand the algorithm’s convergence behavior, track improvements in fitness scores, and observe the distribution of individual scores within each generation.
3.2.1. Gallery Example#
See the following example for practical usage and code details:
Example |
Description |
|---|---|
Demonstrates how to set up and plot the evolution graph of a genetic algorithm optimization process. |
3.2.2. Overview#
The evolution graph highlights key metrics throughout the optimization process.
The mloptimizer library provides a function to generate this graph using Plotly, making it interactive and customizable.
3.2.3. Saved Graph and Data Files#
After the optimization completes, the evolution graph and related data are saved for future reference:
Graph Path: An HTML file of the evolution graph is saved in the graphics directory.
Data Path: CSV files with population data and logbook statistics are saved in the results directory.
For a detailed directory layout, refer to Optimizer Directory Structure.
Note: The evolution graph helps identify whether the genetic algorithm has converged or if additional generations might improve fitness further.