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New diversity measures #198

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188 changes: 188 additions & 0 deletions src/components/measures/diversity.rs
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,11 @@
//! Review and Study of Genotypic Diversity Measures for Real-Coded Representations.
//! IEEE Transactions on Evolutionary Computation 16, 5 (October 2012), 695–710.
//! DOI:<https://doi.org/10/f4ct44>
//!
//! \[3\] A. Mascarenhas, Y. Kobayashi and C. Aranha. 2024.
//! Novel Genotypic Diversity Metrics for Real-Coded Optimization on Multi-Modal Problems.
//! 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, pp. 1-8.
//! DOI: <https://10.1109/CEC60901.2024.10611897>.

use std::{any::type_name, marker::PhantomData};

Expand Down Expand Up @@ -354,3 +359,186 @@ where
diversity_measure(self, problem, state)
}
}

/// Measures the minimum sum of individual distances as described by Mascarenhas et al.
///
/// The value is stored in the [`Diversity<MinimumIndividualDistance>`] state.
#[derive(Clone, Serialize)]
pub struct MinimumIndividualDistance;

impl MinimumIndividualDistance {
pub fn from_params() -> Self {
Self
}

pub fn new<P>() -> Box<dyn Component<P>>
where
P: VectorProblem<Element = f64>,
{
Box::new(Self::from_params())
}
}

impl<P> DiversityMeasure<P> for MinimumIndividualDistance
where
P: VectorProblem<Element = f64>,
{
fn measure(&self, _problem: &P, solutions: &[&Vec<f64>]) -> f64 {
let n = solutions.len();

let mut min_dist = vec![-1.0; n];

for (ind1_i, ind1) in solutions.iter().enumerate() {
for ind2 in solutions.iter() {
if ind1 == ind2 {
continue;
}

let mut sum_d = 0.0;
for (x1, x2) in ind1.iter().zip(ind2.iter()) {
sum_d += (x1 - x2).powi(2);
}
let d = sum_d.sqrt(); // Euclidean distance

if d < min_dist[ind1_i] || min_dist[ind1_i] == -1.0 {
min_dist[ind1_i] = d;
}
}
}
min_dist.iter().sum::<f64>()
}
}

impl<P> Component<P> for MinimumIndividualDistance
where
P: VectorProblem<Element = f64>,
{
fn init(&self, _problem: &P, state: &mut State<P>) -> ExecResult<()> {
state.insert(Diversity::<Self>::new());
Ok(())
}

fn execute(&self, problem: &P, state: &mut State<P>) -> ExecResult<()> {
diversity_measure(self, problem, state)
}
}

/// Measures the radius diversity as described by Mascarenhas et al.
///
/// The value is stored in the [`Diversity<RadiusDiversity>`] state.
///
/// *The code for this measure was generated with the help of ChatGPT (GPT-3.5) using the code
/// provided by Mascarenhas et al. at <https://zenodo.org/records/11077281> and
/// <https://github.com/mascarenhasav/wcci_2024_gdms_paper>.*
#[derive(Clone, Serialize)]
pub struct RadiusDiversity;

impl RadiusDiversity {
pub fn from_params() -> Self {
Self
}

pub fn new<P>() -> Box<dyn Component<P>>
where
P: VectorProblem<Element = f64>,
{
Box::new(Self::from_params())
}
}

impl<P> DiversityMeasure<P> for RadiusDiversity
where
P: VectorProblem<Element = f64>,
{
fn measure(&self, _problem: &P, solutions: &[&Vec<f64>]) -> f64 {
// Calculate the distance matrix using the Euclidean distance
let mut dist_matrix = vec![vec![0.0; solutions.len()]; solutions.len()];
for i in 0..solutions.len() {
for j in 0..solutions.len() {
if i != j {
let sum_sq: f64 = solutions[i]
.iter()
.zip(solutions[j].iter())
.map(|(x1, x2)| (x1 - x2).powi(2))
.sum();
dist_matrix[i][j] = sum_sq.sqrt();
}
}
}

let mut selected_flag = vec![false; solutions.len()];
let original_indices: Vec<usize> = (0..solutions.len()).collect();

let sigma = dist_matrix
.iter()
.flat_map(|row| row.iter())
.cloned()
.fold(f64::NEG_INFINITY, f64::max);
let max_indices: Vec<usize> = dist_matrix
.iter()
.enumerate()
.flat_map(|(i, row)| row.iter().enumerate().map(move |(j, &val)| (i, j, val)))
.filter(|&(_, _, val)| val == sigma)
.map(|(i, _j, _)| i)
.take(2)
.collect();

selected_flag[max_indices[0]] = true;
selected_flag[max_indices[1]] = true;

let mut selected_indices = vec![max_indices[0], max_indices[1]];
let mut sigma_list = vec![0.0, sigma];

while selected_indices.len() < solutions.len() {
let shortest_distances_list: Vec<f64> = (0..solutions.len())
.filter(|&i| !selected_flag[i])
.map(|i| {
(0..solutions.len())
.filter(|&j| selected_flag[j])
.map(|j| dist_matrix[i][j])
.fold(f64::INFINITY, f64::min)
})
.collect();

let max_index = shortest_distances_list
.iter()
.cloned()
.enumerate()
.fold((0, f64::NEG_INFINITY), |(max_idx, max_val), (idx, val)| {
if val > max_val {
(idx, val)
} else {
(max_idx, max_val)
}
})
.0;

let max_original_index = original_indices
.iter()
.filter(|&&idx| !selected_flag[idx])
.nth(max_index)
.unwrap();

selected_flag[*max_original_index] = true;
selected_indices.push(*max_original_index);
let sigma = shortest_distances_list[max_index];
sigma_list.push(sigma);
}

sigma_list.iter().sum()
}
}

impl<P> Component<P> for RadiusDiversity
where
P: VectorProblem<Element = f64>,
{
fn init(&self, _problem: &P, state: &mut State<P>) -> ExecResult<()> {
state.insert(Diversity::<Self>::new());
Ok(())
}

fn execute(&self, problem: &P, state: &mut State<P>) -> ExecResult<()> {
diversity_measure(self, problem, state)
}
}
2 changes: 1 addition & 1 deletion src/components/mutation/de.rs
Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ where

let size = (self.y * 2 + 1) as usize;

if !population.len() % size == 0 {
if population.len() % size != 0 {
return Err(eyre!("the population must be in the format [`y` * 2 + 1]*, where the first individual is the base of the mutation"))
.suggestion("try to use an appropriate selection method for this mutation");
}
Expand Down
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