genetic_algorithm_traits

Trait Individual

Source 
pub trait Individual<'a>: Clone {
    type IndividualCost: 'a;

    // Required methods
    fn mutate(self, prob: f32) -> Self;
    fn crossover(&self, other: &Self) -> Self;
    fn fitness(&self, cost_data: &Self::IndividualCost) -> f64;
}
Expand description

A single instance in the genetic algorithm. In a TSP for example, this would be and individual route.

Required Associated Types§

Source

type IndividualCost: 'a

The Type of cost data this individual is compatible to compute its fitness on.

Required Methods§

Source

fn mutate(self, prob: f32) -> Self

Randomly change the object and therefore it’s fitness This is a key step of the genetic algorithm.

§Arguments
  • prob - The probability with which the individual will mutate.
Source

fn crossover(&self, other: &Self) -> Self

The crossover takes two individual and combines their characteristics. The implementation depends on the problem to be solve with genetic algorithm but both from a performance and runtime perspective, this is one of the most important and time-consuming methods.

§Arguments
  • other - The other individual that should be crossovered with.
Source

fn fitness(&self, cost_data: &Self::IndividualCost) -> f64

How fit is your individual, e.g. how well does it solve the problem you are trying to solve with genetic algorithms. This is the metric that is maximised, e.g. overall individuals with a very high fitness should be found.

§Arguments
  • cost_data - The data that might be needed to compute your fitness. If you use genetic algorithm to solve a traveling salesman problem, the cost_data will typically contain your distance matrix.

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§