metrics.md

1. Introduction
2. Fitness functions
3. Encodings
4. Algorithms
5. Genetic operators
6. Stop conditions
7. Metrics
8. Miscellaneous

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Metrics

By default, when you run the GA, the solve() function returns a set of pareto optimal solutions, but you won't have any additional information about how the population evolved over the run.

In order to get more insight about a run, you can specify a number of metrics that will be tracked throughout the run. Each metric tracks a particular attribute of the population, with the value of the metric being recorded in each generation.

Usage

The tracked metrics must be specified before a run, using the track() method of the GAs. You can specify any number of metrics in the arguments of this function:

// Track the minimum and maximum of the population's fitness values in each
// generation, for each objective
GA.track(metrics::FitnessMin{}, metrics::FitnessMax{})
// Run the GA
GA.solve(...);

After the run, you can access the metrics using the get_metric<MetricType>() method. This will return a reference to the given metric, which contains the values of that metric for every generation:

// Get the metric tracking the minimal fitness values
const auto& fmin = GA.get_metric<metrics::FitnessMin>();

// Print the lowest fitness value of the first objective in the fourth generation
std::cout << fmin[3][0];

Available metrics

There are a number of metrics implemented by the library in the gapp::metrics namespace, see:

• <metrics/fitness_metrics.hpp> for fitness related metrics
• <metrics/distribution_metrics.hpp> for metrics tracking the distribution of the population in the objective space
• <metrics/misc_metrics.hpp> for other types of metrics

All of the metrics that are implemented in the library work for any objective function, regardless of the number of objectives, but the distribution metrics are intended to be used for multi-objective optimization problems.

Custom metrics

If you want to track something that doesn't have a metric already implemented for it, it's possible to implement your own metrics. Metrics must be derived from the metrics::Monitor class, and implement the initialize, and update methods:

// The second type parameter of Monitor is the type used to store the
// gathered metrics. This will be used as the type of the data_ field.
class MyMetric : public metrics::Monitor<MyMetric, std::vector<double>>
{
    // (optional) Initialize the metric. Called at the start of a run.
    void initialize(const GaInfo& ga) override { data_.clear(); }

    // Update the metric with a new value from the current generation.
    // Called once in each generation.
    void update(const GaInfo& ga) override
    {
        // You can access the current state of the GA through
        // the ga parameter.
        ...
        data_.push_back(...);
    }
};

These custom metrics can be used the same way as any other metric already implemented in the library.

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Next: Miscellaneous