Trait metaheuristics_nature::ObjFunc
source · [−]pub trait ObjFunc: Sync + Send {
type Result;
type Fitness: Fitness;
fn fitness(&self, xs: &[f64], f: f64) -> Self::Fitness;
fn result(&self, xs: &[f64]) -> Self::Result;
fn ub(&self) -> &[f64];
fn lb(&self) -> &[f64];
}Expand description
A trait for the objective function.
use metaheuristics_nature::ObjFunc;
struct MyFunc([f64; 3], [f64; 3]);
impl MyFunc {
fn new() -> Self {
Self([0.; 3], [50.; 3])
}
}
impl ObjFunc for MyFunc {
type Result = f64;
type Fitness = f64;
fn fitness(&self, x: &[f64], _: f64) -> Self::Fitness {
x[0] * x[0] + x[1] * x[1] + x[2] * x[2]
}
fn result(&self, xs: &[f64]) -> Self::Result {
self.fitness(xs, 0.)
}
fn ub(&self) -> &[f64] {
&self.1
}
fn lb(&self) -> &[f64] {
&self.0
}
}The objective function returns fitness value that used to evaluate the objective. The lower bound and upper bound represents the number of variables at the same time.
This trait is designed as immutable and there should only has shared data.
Required Associated Types
Required Methods
Return fitness, the smaller value represents a good result.
How to design the fitness value?
Regularly, the evaluation value should not lower than zero, because it is not easy to control with multiplication, and a negative infinity can directly break the result. Instead, a positive enhanced floating point value is the better choice, and the zero is the best result.
In another hand, some marker can help you to compare with other design,
please see Fitness for more information.
Penalty
In another hand, positive infinity represents the worst, or illogical result. In fact, the searching area (or we called feasible solution) should keeping not bad results, instead of evaluating them as the worst one, because of we can keep the searching inspection around the best result, to finding our potential winner.
In order to distinguish how bad the result is, we can add a penalty value, which represents the “fault” on the result.
Under most circumstances, the result is not good enough, appearing on its fitness value. But sometimes a result is badly than our normal results, if we mark them as the worst one (infinity), it will become a great “wall”, which is not suitable for us to search across it.
So that, we use secondary evaluation function to measure the result from other requirements, we call it “constraint” or “penalty function”. The penalty value usually multiply a weight factor for increasing its influence.
Adaptive Value
Sometimes a value that adjust with converge states can help to restrict the searching.
The “adaptive function” can be set in SolverBuilder::adaptive method.
Return the final result of the problem.
The parameters xs is the best parameter we found.