Performance tracking

In MetaGen, every available metaheuristic includes a log_dir parameter that allows users to enable TensorBoard logging for real-time monitoring of key optimization metrics. This feature helps track the optimization process and compare different metaheuristics effectively.

Optional Logging with log_dir

• Users can specify a directory for logging by setting the log_dir parameter when initializing a metaheuristic. If logging is not needed, this parameter can be left as default.

• The logs include information about fitness progression, population size, and the distribution of solutions over iterations.

Required Installation of TensorBoard

To use this feature, the ``tensorboard`` package must be installed. If TensorBoard is not available, logging will be automatically disabled, but the metaheuristic will still function normally.

To install TensorBoard, run:

pip install tensorboard

Visualization of Optimization Metrics

Once the execution of a metaheuristic is complete, users can analyze the results using TensorBoard:

tensorboard --logdir=logs

This will open an interactive dashboard displaying the following metrics:

• Best fitness per iteration – Tracks the highest-performing solution at each step.

• Average fitness per iteration – Shows the trend of overall population performance.

• Fitness distribution per iteration – Provides a histogram of fitness values to analyze variability.

• Population size per iteration – Monitors how the number of evaluated individuals evolves.

• Solution component tracking – Logs numerical values of solution variables to detect structural changes.

• Final best solution summary – Displays key statistics of the best solution found.

By ensuring TensorBoard is installed, users can take advantage of powerful visualizations that provide deeper insights into the optimization process within MetaGen.