/// <summary>
/// generating the initial locations of n flower
/// </summary>
public List <ObjectInstanceSelection> InitializeBat(int nBats, int subsetSize, int probSize, Problem prob)
{
Random rnd = new Random();
List <int> rNum = Training.GetRandomNumbers(probSize, probSize); //generate N random numbers
FireflyInstanceSelection fpa = new FireflyInstanceSelection();
List <ObjectInstanceSelection> attr_values = new List <ObjectInstanceSelection>();
int cnt1 = 0, cnt2 = 0, cnt3 = 0;
//create an array of size n for x and y
int[] xn = new int[subsetSize]; //instance mask
double[] xn_Con = new double[subsetSize]; //instance mask continuous
double freq = new double(); //initialize the frequency of all the bats to zero
double[] vel = new double[subsetSize]; //initialize the velocity of all the bats to zero
int[] pointers = new int[subsetSize]; //array contain pointer to actual individual instance represented in the instance mask
for (int i = 0; i < nBats; i++)
{
xn = new int[subsetSize];
xn_Con = new double[subsetSize];
pointers = new int[subsetSize];
cnt1 = 0; cnt2 = 0; cnt3 = 0;
for (int j = 0; j < prob.Count; j++)
{
if (cnt1 < (0.7 * subsetSize) && prob.Y[rNum[j]] == -1) //select 70% positive instance of the subset
{
xn_Con[cnt3] = rnd.NextDouble();
xn[cnt3] = fpa.Binarize(xn_Con[cnt3], rnd.NextDouble()); //convert generated random number to binary
pointers[cnt3] = rNum[j];
cnt1++; cnt3++;
}
else if (cnt2 < (0.3 * subsetSize) && prob.Y[rNum[j]] == 1)
{
xn_Con[cnt3] = rnd.NextDouble();
xn[cnt3] = fpa.Binarize(xn_Con[cnt3], rnd.NextDouble()); //convert generated random number to binary
pointers[cnt3] = rNum[j];
cnt2++; cnt3++;
}
if (cnt3 >= subsetSize)
{
break;
}
}
ObjectInstanceSelection OI = new ObjectInstanceSelection(xn, xn_Con, freq, vel, pointers, 0.0);
attr_values.Add(OI);
}
return(attr_values);
}
public Problem Bat(Problem prob)
{
//default parameters
int populationSize = 5; //number of bats in the population
int maxGeneration = 100;
int subsetSize = 200;
double loudness = 0.5;
double pulseRate = 0.5;
int totalInstances = prob.X.Count(); //problem size
double frequencyMin = 0; //minimum frequency. Frequency range determine the scalings
double frequencyMax = 2; //maximum frequency.
int lowerBound = -2; //set lower bound - lower boundary
int upperBound = 2; //set upper bound - upper boundary
double[] batFitnessVal = new double[populationSize];
double[] newbatFitnessVal = new double[populationSize];
double globalBest = double.MinValue;
ObjectInstanceSelection globalBestBat = null;
Random r = new Random();
//initialize population
List <ObjectInstanceSelection> bats = InitializeBat(populationSize, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newBats = new List <ObjectInstanceSelection>(bats.Count); //create a clone of bats
bats.ForEach((item) =>
{
newBats.Add(new ObjectInstanceSelection(item.__Attribute_Values, item.__Attribute_Values_Continuous, item.__Frequency, item.__Velocity, item.__Pointers, item.__Fitness)); //create a clone of flowers
});
batFitnessVal = fi.EvaluateObjectiveFunction(bats, prob); //evaluate fitness value for all the bats
newbatFitnessVal = fi.EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for new bats. Note: this will be the same for this function call, since pollination has not occur
BatFitness(batFitnessVal, bats); //fitness value for each bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.__Fitness;
//start bat algorithm
double rand = r.NextDouble(); //generate random number
for (int i = 0; i < maxGeneration; i++)
{
//loop over all bats or solutions
for (int j = 0; j < populationSize; j++)
{
bats[j].__Frequency = frequencyMin + (frequencyMin - frequencyMax) * rand; //adjust frequency
for (int k = 0; k < subsetSize; k++)
{
double randNum = SimpleRNG.GetNormal(); //generate random number with normal distribution
newBats[j].__Velocity[k] = bats[j].__Velocity[k] + (bats[j].__Attribute_Values_Continuous[k] - globalBestBat.Attribute_Values_Continuous[k]) * bats[j].__Frequency; //update velocity
newBats[j].__Attribute_Values_Continuous[k] = bats[j].__Attribute_Values_Continuous[k] + bats[j].__Velocity[k]; //update bat position in continuous space
newBats[j].__Attribute_Values_Continuous[k] = SimpleBounds(newBats[j].__Attribute_Values_Continuous[k], lowerBound, upperBound); //ensure that value does not go beyond defined boundary
if (rand > pulseRate) //The factor 0.001 limits the step sizes of random walks
{
newBats[j].__Attribute_Values_Continuous[k] = globalBestBat.Attribute_Values_Continuous[k] + 0.001 * randNum;
}
newBats[j].__Attribute_Values[k] = fi.Binarize(newBats[j].__Attribute_Values_Continuous[k], r.NextDouble()); //convert to binary
}
}
//evaluate new solution
newbatFitnessVal = fi.EvaluateObjectiveFunction(newBats, prob); //evaluate fitness value for all the bats
BatFitness(newbatFitnessVal, newBats); //fitness value for new bats
globalBestBat = EvaluateSolution(batFitnessVal, newbatFitnessVal, globalBest, bats, newBats, globalBestBat, loudness); //get the global best flower
globalBest = globalBestBat.__Fitness;
}
//ensure that at least, 40 instances is selected for classification
int countSelected = globalBestBat.__Attribute_Values.Count(q => q == 1); //count the total number of selected instances
int diff, c = 0, d = 0;
int Min = 40; //minimum number of selected instances
if (countSelected < Min)
{
//if there are less than N, add N instances, where N = the number of selected instances
diff = Min - countSelected;
while (c < diff)
{
if (globalBestBat.__Attribute_Values[d++] == 1)
{
continue;
}
else
{
globalBestBat.__Attribute_Values[d++] = 1;
c++;
}
}
}
Problem subBest = fi.buildModel(globalBestBat, prob); //build model for the best Instance Mast
return(subBest);
}
//flower pollination algorithm by Yang
public Problem FlowerPollination(Problem prob)
{
int nargin = 0, totalInstances = prob.X.Count(), maxGeneration = 500;
int numOfFlower = 10; //population size
double probabilitySwitch = 0.8; //assign probability switch
int subsetSize = 200; //dimension for each flower
double[] flowerFitnessVal = new double[numOfFlower];
double[] newFlowerFitnessVal = new double[numOfFlower];
FireflyInstanceSelection fw = new FireflyInstanceSelection();
double globalBest = double.MinValue;
double newBest = new double();
ObjectInstanceSelection globalBestFlower = null;
int lowerBound = -2; //set lower bound - lower boundary
int upperBound = 2; //set upper bound - upper boundary
int maxIndex;
//inittalize flowers, and get global best
List <ObjectInstanceSelection> flowers = InitializeFlower(numOfFlower, subsetSize, totalInstances, prob); //initialize solution
List <ObjectInstanceSelection> newFlowers = new List <ObjectInstanceSelection>(flowers.Count); //create a clone of flowers
flowers.ForEach((item) =>
{
newFlowers.Add(new ObjectInstanceSelection(item.__Attribute_Values, item.__Attribute_Values_Continuous, item.__Pointers, item.__Fitness)); //create a clone of flowers
});
flowerFitnessVal = fw.EvaluateObjectiveFunction(flowers, prob); //evaluate fitness value for all the flowers
newFlowerFitnessVal = fw.EvaluateObjectiveFunction(newFlowers, prob); //evaluate fitness value for new flowers. Note: this will be the same for this function call, since pollination has not occur
FlowerFitness(flowerFitnessVal, flowers); //fitness value for each flower
FlowerFitness(newFlowerFitnessVal, newFlowers); //fitness value for new flower
globalBestFlower = EvaluateSolution(flowerFitnessVal, newFlowerFitnessVal, globalBest, flowers, newFlowers, globalBestFlower); //get the global best flower
globalBest = flowerFitnessVal.Max();
//start flower algorithm
Random r = new Random();
double[] levy = new double[subsetSize];
for (int i = 0; i < maxGeneration; i++)
{
double rand = r.NextDouble();
if (rand > probabilitySwitch) //global pollination
{
//global pollination
for (int j = 0; j < numOfFlower; j++)
{
levy = LevyFlight(subsetSize);
for (int k = 0; k < subsetSize; k++)
{
double A = levy[k] * (flowers[j].__Attribute_Values_Continuous[k] - globalBestFlower.__Attribute_Values_Continuous[k]);
double B = flowers[j].__Attribute_Values_Continuous[k] + A;
A = SimpleBounds(B, lowerBound, upperBound); //ensure that value does not go beyond defined boundary
newFlowers[j].__Attribute_Values_Continuous[k] = A;
newFlowers[j].__Attribute_Values[k] = fw.Binarize(B, r.NextDouble()); //convert to binary
}
}
}
else //local pollination
{
for (int j = 0; j < numOfFlower; j++)
{
List <int> randNum = Training.GetRandomNumbers(2, numOfFlower); //generate 2 distinct random numbers
double epsilon = rand;
//local pollination
for (int k = 0; k < subsetSize; k++)
{
double A = flowers[j].__Attribute_Values_Continuous[k] + epsilon * (flowers[randNum[0]].__Attribute_Values_Continuous[k] - flowers[randNum[1]].__Attribute_Values_Continuous[k]); //randomly select two flowers from neighbourhood for pollination
A = SimpleBounds(A, lowerBound, upperBound); //ensure that value does not exceed defined boundary
newFlowers[j].__Attribute_Values_Continuous[k] = A; //save computation
newFlowers[j].__Attribute_Values[k] = fw.Binarize(A, r.NextDouble()); //convert to binary
}
}
}
//evaluate new solution
newFlowerFitnessVal = fw.EvaluateObjectiveFunction(newFlowers, prob); //evaluate fitness value for all the flowers
FlowerFitness(newFlowerFitnessVal, newFlowers); //fitness value for new flower
globalBestFlower = EvaluateSolution(flowerFitnessVal, newFlowerFitnessVal, globalBest, flowers, newFlowers, globalBestFlower); //Evaluate solution, update better solution and get global best flower
globalBest = flowerFitnessVal.Max();
}
//ensure that at least, 40 instances is selected for classification
int countSelected = globalBestFlower.__Attribute_Values.Count(q => q == 1); //count the total number of selected instances
int diff, c = 0, d = 0;
int Min = 40; //minimum number of selected instances
if (countSelected < Min)
{
//if there are less than N, add N instances, where N = the number of selected instances
diff = Min - countSelected;
while (c < diff)
{
if (globalBestFlower.__Attribute_Values[d++] == 1)
{
continue;
}
else
{
globalBestFlower.__Attribute_Values[d++] = 1;
c++;
}
}
}
Problem subBest = fw.buildModel(globalBestFlower, prob); //build model for the best Instance Mast
return(subBest);
}
public Problem SocialSpider(Problem prob, out double storagePercentage)
{
int nSpiders = 5; //population size of spiders
int subsetSize = 100;
int totalInstances = prob.X.Count();;
int bound = 100, maxGen = 5;
double r_a = 1; //This parameter controls the attenuation rate of the vibration intensity over distance
double p_c = 0.7; // p_c describes the probability of changing mask of spider
double p_m = 0.1; // This is also a user-controlled parameter defined in (0, 1). It controls the probability of assigning a one or zero to each bit of a mask
bool info = true;
double[][] globalBestPosition = new double[1][];
double[] targetIntensity = new double[nSpiders]; //best vibration for each spider
//double[] targetPosition = new double[nSpiders]; //target position for each spider
double[,] mask = new double[nSpiders, subsetSize];
double[,] newMask = new double[nSpiders, subsetSize];
double[,] movement = new double[nSpiders, subsetSize];
double[] inactive = new double[nSpiders];
double[] spiderFitnessVal = new double[nSpiders];
double[] newSpiderFitnessVal = new double[nSpiders];
ObjectInstanceSelection globalBestSpider = null;
double globalBest = double.MinValue;
Random rand = new Random();
FlowerPollinationAlgorithm fpa = new FlowerPollinationAlgorithm();
//initialize population
List <ObjectInstanceSelection> spiders = InitializeBinarySpider(nSpiders, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newSpiders = new List <ObjectInstanceSelection>(spiders.Count); //create a clone of bats
spiders.ForEach((item) =>
{
newSpiders.Add(new ObjectInstanceSelection(item.Attribute_Values, item.Attribute_Values_Continuous, item.Pointers, item.Fitness, item.Position)); //create a clone of flowers
});
spiderFitnessVal = EvaluateObjectiveFunction(spiders, prob); //evaluate fitness value for all the bats
newSpiderFitnessVal = EvaluateObjectiveFunction(newSpiders, prob); //evaluate fitness value for new spiders. Note: this will be the same for this function call, since pollination has not occur
SpiderFitness(spiderFitnessVal, spiders); //fitness value for each spiders
SpiderFitness(newSpiderFitnessVal, newSpiders); //fitness value for new spider
globalBestSpider = EvaluateSolution(spiderFitnessVal, newSpiderFitnessVal, globalBest, spiders, newSpiders, globalBestSpider); //get the global best spider
globalBest = globalBestSpider.Fitness;
double[] standDev = new double[subsetSize];
List <double> listPositions = new List <double>();
List <double[]> spiderPositions = new List <double[]>();
//calculate the standard deviation of all spider positions
for (int a = 0; a < subsetSize; a++)
{
double[] sPositions = new double[nSpiders];
for (int b = 0; b < nSpiders; b++)
{
sPositions[b] = spiders[b].Attribute_Values[a]; //get all spider positions column wise
//sPositions[b] = spiders[b].Attribute_Values_Continuous[a]; //get all spider positions column wise
}
spiderPositions.Add(sPositions); //save positions in list
}
for (int a = 0; a < subsetSize; a++)
{
standDev[a] = getStandardDeviation(spiderPositions[a].ToList()); //calculate standard deviation for each spider solution
}
double baseDistance = standDev.Average(); //calculate the mean of standev
//compute paired euclidean distances of all vectors in spider; similar to pdist function in matlab. Reference: http://www.mathworks.com/help/stats/pdist.html
int n = (nSpiders * (nSpiders - 1)) / 2; //total number of elements array dist.
double[] euclidenDist = new double[n]; //Note that, this is array for paired eucliden distance, similar to pdist() function in matlab.
int kk = 0;
for (int i = 0; i < nSpiders; i++)
{
for (int j = 1 + i; j < nSpiders; j++)
{
//this distance is in pairs -> 1,0; 2,0; 3,0,...n,0; 2,1; 3,1; 4,1,...n,1;.... It is similar to pdist function in matlab
//euclidenDist[kk++] = computeEuclideanDistance(spiders[j].Attribute_Values_Continuous, spiders[i].Attribute_Values_Continuous); //generate a vibration for each spider position
euclidenDist[kk++] = computeEuclideanDistance(spiders[j].Attribute_Values, spiders[i].Attribute_Values); //generate a vibration for each spider position
//distance[i][j] = computeEuclideanDistance(spiders[i].Attribute_Values, spiders[j].Attribute_Values);
}
}
double[,] distance = SquareForm(euclidenDist, nSpiders); //Convert vibration to square matix, using SquareForm() function in matlab. Reference: see Squareform function in google
//double[,] intensityReceive = new double[nSpiders, nSpiders];
double[][] intensityReceive = new double[nSpiders][];
for (int a = 0; a < maxGen; a++)
{
for (int j = 0; j < nSpiders; j++)
{
//calculate the intensity for all the generated vibrations
intensityReceive[j] = new double[nSpiders];
double A = (spiders[j].Fitness + Math.Exp(-100)) + 1;
double intensitySource = Math.Log(1 / A);
for (int k = 0; k < nSpiders; k++)
{
double intensityAttenuation = Math.Exp(-distance[j, k] / (baseDistance * r_a));
//intensityReceive[j, k] = intensitySource * intensityAttenuation; //intensity for each spider vibration
intensityReceive[j][k] = intensitySource * intensityAttenuation; //intensity for each spider vibration
}
}
//select strongest vibration from intensity
int row = intensityReceive.GetLength(0);
int column = intensityReceive[0].Count();
//IEnumerable<double> bestReceive = Enumerable.Range(0, row).Select(i => Enumerable.Range(0, column).Select(j => intensityReceive[i, j]).Max()); //get the max value in each row
IEnumerable <double> bestReceive = Enumerable.Range(0, row).Select(i => Enumerable.Range(0, column).Select(j => intensityReceive[i][j]).Max()); //get the max value in each row
//IEnumerable<int> bestReceiveIndex = Enumerable.Range(0, row).Select(i => Enumerable.Range(0, column).Select(j => intensityReceive[i, j]).Max()); //get the max value in each row
//get the index of the strongest vibration
int[] maxIndex = new int[nSpiders];
for (int i = 0; i < nSpiders; i++)
{
maxIndex[i] = Array.IndexOf(intensityReceive[i], bestReceive.ElementAt(i));
}
//Store the current best vibration
int[] keepTarget = new int[nSpiders];
int[] keepMask = new int[nSpiders];
double[,] targetPosition = new double[nSpiders, subsetSize];
for (int i = 0; i < nSpiders; i++)
{
if (bestReceive.ElementAt(i) <= targetIntensity[i])
{
keepTarget[i] = 1;
}
inactive[i] = inactive[i] * keepTarget[i] + keepTarget[i];
targetIntensity[i] = (targetIntensity[i] * keepTarget[i]) + bestReceive.ElementAt(i) * (1 - keepTarget[i]);
if (rand.NextDouble() < Math.Pow(p_c, inactive[i]))
{
keepMask[i] = 1;
}
inactive[i] = inactive[i] * keepMask[i];
for (int j = 0; j < subsetSize; j++)
{
//newSpiders[i].Attribute_Values[j] = fi.Binarize(newSpiders[i].Attribute_Values[j] * spiders[maxIndex[i]].Attribute_Values[j] * (1 - keepTarget[i]), rand.NextDouble()); //update solution
targetPosition[i, j] = targetPosition[i, j] * keepTarget[i] + spiders[maxIndex[i]].Attribute_Values[j] * (1 - keepTarget[i]);
//targetPosition[i, j] = targetPosition[i, j] * keepTarget[i] + spiders[maxIndex[i]].Attribute_Values_Continuous[j] * (1 - keepTarget[i]);
newMask[i, j] = Math.Ceiling(rand.NextDouble() + rand.NextDouble() * p_m - 1);
mask[i, j] = keepMask[i] * mask[i, j] + (1 - keepMask[i]) * newMask[i, j]; //update dimension mask of spider
}
}
//Reshuffule the Spider solution
//Method: randomly generated positions pointing to rows and columns in the solution space. With the pointers, we can acess indivdual indices(or positions) in the solution
double[,] randPosition = GenerateRandomSpiderPosition(nSpiders, subsetSize, spiders);
//generate psfo, and perform random walk
double[,] followPosition = new double[nSpiders, subsetSize];
for (int i = 0; i < nSpiders; i++)
{
for (int j = 0; j < subsetSize; j++)
{
followPosition[i, j] = mask[i, j] * randPosition[i, j] + (1 - mask[i, j]) * targetPosition[i, j];
movement[i, j] = rand.NextDouble() * movement[i, j] + (followPosition[i, j] - spiders[i].Attribute_Values[j]) * rand.NextDouble(); //perform random movement
//movement[i, j] = rand.NextDouble() * movement[i, j] + (followPosition[i, j] - spiders[i].Attribute_Values_Continuous[j]) * rand.NextDouble(); //perform random movement
//newSpiders[i].Attribute_Values[j] = fi.Binarize(newSpiders[i].Attribute_Values_Continuous[j] + movement[i, j], rand.NextDouble()); //actual random walk
newSpiders[i].Attribute_Values[j] = fi.Binarize(newSpiders[i].Attribute_Values[j] + movement[i, j], rand.NextDouble()); //actual random walk
}
}
//Select best solutions from the original population and matured population for the next generation;
fpa.SelectBestSolution(spiders, newSpiders);
//evaluate new solution
newSpiderFitnessVal = EvaluateObjectiveFunction(newSpiders, prob); //evaluate fitness value for all the bats
SpiderFitness(newSpiderFitnessVal, newSpiders); //fitness value for new bats
globalBestSpider = EvaluateSolution(spiderFitnessVal, newSpiderFitnessVal, globalBest, spiders, newSpiders, globalBestSpider); //get the global best flower
globalBest = globalBestSpider.Fitness;
//if solution has converged to a optimal user-defined point, stop search
int Max = 60; // maximum percentage reduction
if (globalBest >= Max) //if the percentage reduction has approached 60%, stop search!
{
break;
}
}
//ensure that at least, N instances are selected for classification
int Min = 15; //minimum number of selected instances
globalBestSpider = fpa.AddInstances(globalBestSpider, Min);
Problem subBest = fi.buildModelMultiClass(globalBestSpider, prob); //build model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
public Problem CuckooSearch(Problem prob, out double storagePercentage)
{
int nNests = 5; //number of nests, or number of solutions
int subsetSize = 100;
int maxGen = 5; //maximum generation
double discoveryRate = 0.25; //discovery rate of alien eggs
double tolerance = Math.Exp(-5);
int lowerBound = -5;
int upperBound = 5;
int totalInstances = prob.X.Count(); //problem size
double[] cuckooFitnessVal = new double[nNests];
double[] newCuckooFitnessVal = new double[nNests];
ObjectInstanceSelection globalBestCuckoo = null;
double globalBest = double.MinValue;
Random rand = new Random();
FlowerPollinationAlgorithm fpa = new FlowerPollinationAlgorithm();
//initialize population
List <ObjectInstanceSelection> cuckoos = InitializeBinaryCuckoo(nNests, subsetSize, totalInstances, prob);
List <ObjectInstanceSelection> newCuckoos = new List <ObjectInstanceSelection>(cuckoos.Count); //create a clone of bats
cuckoos.ForEach((item) =>
{
newCuckoos.Add(new ObjectInstanceSelection(item.Attribute_Values, item.Attribute_Values_Continuous, item.Pointers, item.Fitness)); //create a clone of flowers
});
cuckooFitnessVal = EvaluateObjectiveFunction(cuckoos, prob); //evaluate fitness value for all the bats
newCuckooFitnessVal = EvaluateObjectiveFunction(newCuckoos, prob); //evaluate fitness value for new bats. Note: this will be the same for this function call, since pollination has not occur
CuckooFitness(cuckooFitnessVal, cuckoos); //fitness value for each bats
CuckooFitness(newCuckooFitnessVal, newCuckoos); //fitness value for new bats
globalBestCuckoo = EvaluateSolution(cuckooFitnessVal, newCuckooFitnessVal, globalBest, cuckoos, newCuckoos, globalBestCuckoo); //get the global best flower
globalBest = globalBestCuckoo.__Fitness;
//generate new solutions
double beta = 3 / 2;
double A = fp.Gamma(1 + beta) * Math.Sin(Math.PI * (beta / 2));
double B = fp.Gamma((1 + beta) / 2) * beta;
double C = (beta - 1) / 2;
double D = Math.Pow(2, C);
double E = A / (B * D);
double sigma = Math.Pow(E, (1 / beta));
double F;
double G;
double step;
double stepSize;
int x = 0;
for (int i = 0; i <= maxGen; i++)
{
for (int j = 0; j < nNests; j++)
{
for (int k = 0; k < subsetSize; k++)
{
F = SimpleRNG.GetNormal() * sigma;
G = SimpleRNG.GetNormal();
step = F / Math.Pow(Math.Abs(G), (1 / beta));
//In the next equation, the difference factor (s-best) means that when the solution is the best solution, it remains unchanged.
//Here the factor 0.01 comes from the fact that L/100 should the typical step size of walks/flights where L is the typical lenghtscale;
//otherwise, Levy flights may become too aggresive/efficient, which makes new solutions (even) jump out side of the design domain (and thus wasting evaluations).
stepSize = 0.01 * step * (cuckoos[j].Attribute_Values[k] - globalBestCuckoo.Attribute_Values[k]);
//Now the actual random walks or levyy flights
newCuckoos[j].Attribute_Values[k] = fi.Binarize((newCuckoos[j].Attribute_Values[k] + stepSize) * SimpleRNG.GetNormal(), rand.NextDouble());
if (cuckoos[j].Attribute_Values[k] == 1 && newCuckoos[j].Attribute_Values[k] == 0)
{
x++;
}
}
}
//discovery and randomization - replace some nest by constructing new solutions
newCuckoos = EmptyNest(cuckoos, newCuckoos, discoveryRate, subsetSize, nNests);
//Select best solutions from the original population and matured population for the next generation;
fpa.SelectBestSolution(cuckoos, newCuckoos);
//evaluate new solution
newCuckooFitnessVal = EvaluateObjectiveFunction(newCuckoos, prob); //evaluate fitness value for all the bats
CuckooFitness(newCuckooFitnessVal, newCuckoos); //fitness value for new bats
globalBestCuckoo = EvaluateSolution(cuckooFitnessVal, newCuckooFitnessVal, globalBest, cuckoos, newCuckoos, globalBestCuckoo); //get the global best flower
globalBest = globalBestCuckoo.Fitness;
//if solution has converged to a optimal user-defined point, stop search
int Max = 60; // maximum percentage reduction
if (globalBest >= Max) //if the percentage reduction has approached 60%, stop search!
{
break;
}
}
//ensure that at least, N instances are selected for classification
int min = 40; //minimum number of selected instances
globalBestCuckoo = fpa.AddInstances(globalBestCuckoo, min);
Problem subBest = fi.buildModelMultiClass(globalBestCuckoo, prob); //build model for the best Instance Mast
storagePercentage = Training.StoragePercentage(subBest, prob); //calculate the percent of the original training set was retained by the reduction algorithm
return(subBest);
}
