I tried running my code but I got an error.

the error said:

tempCodeRunnerFile.cpp:85:42: error: expected expression
sort(indices.begin(), indices.end(), [&](int a, int b) {

im running this code on vscode (MacOS) . I have set up my vscode, but im not sure if there’s anything that I missed.

the code im trying to run:
#include <iostream>
#include <vector>
#include <cmath>
#include <cstdlib>
#include <ctime>
#include <algorithm>
#include <numeric> // For std::iota

using namespace std;

const int populationSize = 100; // Size of the population
const int chromosomeLength = 10; // Length of each chromosome
const double crossoverProbability = 0.8; // Probability of crossover
const int numberOfOffspring = 10; // Number of offspring to produce

vector<vector<double> > chromosomes; // Population of chromosomes

// Function to calculate fitness based on Schwefel function
double calculateFitness(const vector<double>& chromosome) {
    double sum = 0.0;
    int d = chromosome.size();
    for (int i = 0; i < d; ++i) {
    sum += chromosome[i] * sin(sqrt(fabs(chromosome[i])));
}
return 418.9829 * d - sum;
}

// Function to calculate fitness for the entire population
vector<double> calculatePopulationFitness(const vector<vector<double> >& population) {
vector<double> fitness(population.size());
for (size_t i = 0; i < population.size(); ++i) {
    fitness[i] = calculateFitness(population[i]);
}
return fitness;
}

// Function to calculate average fitness of the population
double calculateAverageFitness(const vector<double>& fitness) {
double totalFitness = accumulate(fitness.begin(), fitness.end(), 0.0);
return totalFitness / fitness.size();
}

// Two-point crossover function
void twoPointCrossover(int parent1Index, int parent2Index, vector<vector<double> >& offspring) {
// Randomly select two crossover points
int crossoverPoint1 = rand() % chromosomeLength;
int crossoverPoint2 = rand() % chromosomeLength;

// Ensure crossoverPoint1 is less than crossoverPoint2
if (crossoverPoint1 > crossoverPoint2) {
    swap(crossoverPoint1, crossoverPoint2);
}

// Perform crossover
vector<double> offspring0 = chromosomes[parent1Index];
vector<double> offspring1 = chromosomes[parent2Index];
for (int i = crossoverPoint1; i < crossoverPoint2; ++i) {
    swap(offspring0[i], offspring1[i]);
}

// Add offspring to the offspring vector
offspring.push_back(offspring0);
offspring.push_back(offspring1);
}

// Mutation function
void mutate(vector<double>& chromosome) {
// Randomly select two genes to mutate
int mutationGeneIndex1 = rand() % chromosomeLength;
int mutationGeneIndex2 = rand() % chromosomeLength;

// Mutate the selected genes
chromosome[mutationGeneIndex1] = ((rand() / (double)RAND_MAX) * 10.0) - 5.0;
chromosome[mutationGeneIndex2] = ((rand() / (double)RAND_MAX) * 10.0) - 5.0;
}

// Function to replace the least fit individuals in the population with offspring (assuming we want to lower the average fitness)
void steadyStateSelection(vector<vector<double> >& population, const vector<vector<double> >& offspring) {
// Calculate fitness of the population
vector<double> fitness = calculatePopulationFitness(population);

// Create a vector of indices sorted by fitness (ascending)
vector<int> indices(population.size());
iota(indices.begin(), indices.end(), 0); // Initialize with 0, 1, 2, ..., populationSize-1
sort(indices.begin(), indices.end(), [&](int a, int b) {
    return fitness[a] < fitness[b]; // Sort by ascending fitness
});

    // Replace the least fit individuals with the offspring
    for (size_t i = 0; i < offspring.size(); ++i) {
        population[indices[indices.size() - 1 - i]] = offspring[i];
    }
}

int main() {
    // Initialize random seed
    srand(static_cast<unsigned int>(time(NULL)));

    // Generate initial population of chromosomes
    for (int i = 0; i < populationSize; ++i) {
        vector<double> chromosome(chromosomeLength);
        for (int j = 0; j < chromosomeLength; ++j) {
            chromosome[j] = ((rand() / (double)RAND_MAX) * 10.0) - 5.0; // Random gene value between -5 and 5
        }
        chromosomes.push_back(chromosome);
    }

    // Calculate initial fitness and average fitness
    vector<double> initialFitness = calculatePopulationFitness(chromosomes);
    double initialAverageFitness = calculateAverageFitness(initialFitness);

   // Output original population before survivor selection
    cout << "Original Population Before Survivor Selection:" << endl;
    for (size_t i = 0; i < chromosomes.size(); ++i) {
    cout << "Individual " << i << ": ";
    for (double gene : chromosomes[i]) {
        cout << gene << " ";
    }
    cout << endl;
    }
    cout << endl;
    for (size_t i = 0; i < initialFitness.size(); ++i) {
    cout << "Fitness of Individual " << i << ": " << initialFitness[i] << endl;
    }

    // Select pairs of parents and perform crossover
    vector<vector<double> > offspring;
    for (int i = 0; i < numberOfOffspring; ++i) {
    // Randomly select two parents
    int parent1Index = rand() % populationSize;
    int parent2Index = rand() % populationSize;

    // Perform crossover with probability crossoverProbability
    if ((rand() / (double)RAND_MAX) < crossoverProbability) {
        twoPointCrossover(parent1Index, parent2Index, offspring);

        // Mutation
        mutate(offspring[offspring.size() - 2]);
        mutate(offspring[offspring.size() - 1]);
        }
    }

    // Replace least fit individuals in the population with offspring
    steadyStateSelection(chromosomes, offspring);

    // Calculate new fitness and average fitness
    vector<double> newFitness = calculatePopulationFitness(chromosomes);
    double newAverageFitness = calculateAverageFitness(newFitness);

    // Output new population after survivor selection
    cout << endl << "New Population After Survivor Selection:" << endl;
    for (size_t i = 0; i < chromosomes.size(); ++i) {
    cout << "Individual " << i << ": ";
    for (double gene : chromosomes[i]) {
        cout << gene << " ";
    }
    cout << endl ;
    }
    cout << endl;
    for (size_t i = 0; i < newFitness.size(); ++i) {
    cout <<  "Fitness of Individual " << i << ": " << newFitness[i] << endl;
    }
    cout << endl << "Average Fitness Before Survivor Selection: " << initialAverageFitness << endl;
    cout << "Average Fitness After Survivor Selection: " << newAverageFitness << endl;

    return 0;
    }

fix the error and find out if I made a mistake during the setup of my vscode.