Genetic Algorithm Based on Natural Selection Theory for Optimization Problems
Abstract
:1. Introduction
2. Materials and Methods
2.1. Genetic Algorithm
2.2. Genetic Algorithm Based on Natural Selection Theory (GABONST)
 Beginning of the algorithm.
 Set number of population n and number of iteration NumIter.
 Generate the population (chromosomes (S)) randomly; where S = {s_{1}, s_{2}, …, s_{n}}.
 Calculate the fitness value of each chromosome in the population g(S).
 Calculate the mean of the fitness values using Equation (1).$$\mathrm{Mean}=\frac{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{g}({\mathrm{s}}_{\mathrm{i}})}{\mathrm{n}}$$
 6.
 Compare the fitness value of each chromosome g(s_{i}) with the mean:
 If g(s_{i}) is less or equal to the mean then implement the mutation operation on the s_{i} and move to the next generation. This represents the right side of the GABONST flowchart (see Figure 2), where the right side simulates the wellqualified organisms (chromosomes) to survive the current environment.
 Otherwise, the chromosome s_{i} will get two chances to be improved, this represents the left side of the GABONST flowchart (see Figure 2), where the left side simulates the idea of giving the unqualified organisms (chromosomes) two chances to adjust their genes and be qualified to survive the current environment:
 i.
 The first chance is through getting married to a wellqualified organism (crossover the weak chromosome (s_{i}) with a wellqualified chromosome (RS)). If the new chromosome (s_{i}, _{new})_{C}, which is obtained by crossover s_{i} and RS, qualifies to survive the current environment (g(s_{i}, _{new})_{C} less or equal to the mean) then the (s_{i}, _{new})_{C} move to the next generation. Otherwise, go to the second chance, step (ii).
 ii.
 The second chance is through the genetic mutation (implement the mutation operation to the weak chromosome (s_{i})). If the new chromosome (s_{i}, _{new})_{M}, which is obtained by applying the mutation operation on s_{i}, qualifies to survive the current environment (g(s_{i}, _{new})_{M} less or equal to the mean) then the (s_{i}, _{new})_{M} move to the next generation. Otherwise, in the case that the organism (chromosome (s_{i})) has missed both of the chances to be qualified to survive in the current environment then that organism will die (that chromosome (s_{i}) will be deleted) and a new one comes to life (add a random generated chromosome to the next generation). Figure 3 provides an example of the arithmetic crossover and uniform mutation operations that have been applied in GABONST.
Algorithm 1 GABONST 

3. Results
3.1. Experimental Test One
3.2. Experimental Test Two
3.2.1. Basic ELM
3.2.2. GABONST–ELM
 w_{ij} $\in $ [−1, 1], is the input weight value that connect ith hidden node and jth input node
 b_{i} $\in $ [0, 1] = ith hidden node bias
 n = input node numbers
 m = hidden node numbers
 m × (n + 1) represents the chromosome’s dimension, hence requiring parameter optimizations. Therefore, the fitness function in the GABONST–ELM set is calculated utilizing Equation (6).
 $\mathsf{\beta}$: output weight matrix
 y_{j}: true value
 N: number of training samples
 A.
 The arithmetical crossover operation is used for exchanging information between that chromosome and a randomly selected chromosome from the top five chromosomes of the current population. The new offspring will be compared to the mean:If it is equal to or less than the mean then move the new offspring into the new generation.If it is greater than the mean then implement step B.
 B.
 The uniform mutation operation is applied to change the genes of that chromosome and generate a new chromosome. The new chromosome will be compared to the mean: if it is equal to or less than the mean then move it into the new generation. If it is greater than the mean then delete that chromosome and add a randomly generated chromosome.
3.2.3. LID Dataset
 The sampling rate is 44,100 Hz, based on the Nyquist frequency the highest frequency was 22,050 Hz. The length of 30 s utterance was approximately 1,323,000 (44,100 * 30) samples.
 Quantization: represents realvalued numbers as integers of a 16bit range (values from −32,768 to 32,767). The following is a depiction of the utilized dataset:
 Name and extension of the dataset: iVectors.mat;
 Dimension of the dataset is depicted in Table 6;Table 6. Dataset dimension [29].
Total Utterance Number Total Class Number iVector Features Dimension of One Utterance 120 8 600  Depiction of the class is shown in Table 7;Table 7. Depiction of the class [29].
No Class Name Utterance Number 1 Arabic 15 2 English 15 3 Malay 15 4 French 15 5 Spanish 15 6 German 15 7 Persian 15 8 Urdu 15  Feature depiction (as depicted in Table 8);Table 8. Feature depiction [29].
No Features Name Features Type 1→600 ivector values Single  The label of the class: last column (column number 601).
3.2.4. Evaluation of the Different Learning Model Parameters
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Objective Function  Dim  Range  Optimal Solution 

${\mathrm{f}}_{1}\left(\mathrm{x}\right)=\frac{1}{\mathrm{d}}{\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{d}}}{\mathrm{sin}}^{6}\left(5{\mathsf{\pi}\mathrm{x}}_{\mathrm{i}}\right)$  10  [−1, 1]  −1 
${\mathrm{f}}_{2}\left(\mathrm{x}\right)={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{d}}}\mathrm{sin}\left({\mathrm{x}}_{\mathrm{i}}\right){\mathrm{sin}}^{2\mathrm{m}}\left(\frac{{\mathrm{ix}}_{\mathrm{i}}^{2}}{\mathsf{\pi}}\right)$  2  [0, π]  −1.8013 
${\mathrm{f}}_{3}\left(\mathrm{x}\right)={\left({\mathrm{x}}_{1}+2{\mathrm{x}}_{2}7\right)}^{2}+{\left(2{\mathrm{x}}_{1}+{\mathrm{x}}_{2}5\right)}^{2}$  2  [−10, 10]  0 
${\mathrm{f}}_{4}\left(\mathrm{x}\right)=\frac{1}{2}{\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{d}}}\left({\mathrm{x}}_{\mathrm{i}}^{4}16{\mathrm{x}}_{\mathrm{i}}^{2}+5{\mathrm{x}}_{\mathrm{i}}\right)$  10  [−5, 5]  −391.6599 
${\mathrm{f}}_{5}\left(\mathrm{x}\right)={\displaystyle {\displaystyle \prod}_{\mathrm{i}=1}^{\mathrm{d}}}\sqrt{{\mathrm{x}}_{\mathrm{i}}}\mathrm{sin}\left({\mathrm{x}}_{\mathrm{i}}\right)$  2  [0, 10]  −6.1295 
${\mathrm{f}}_{6}\left(\mathrm{x}\right)={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{x}}_{\mathrm{i}}^{2}$  256  [−5.12, 5.12]  0 
${\mathrm{f}}_{7}\left(\mathrm{x}\right)={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}1}}\left[100{\left({\mathrm{x}}_{\mathrm{i}+1}{\mathrm{x}}_{\mathrm{i}}^{2}\right)}^{2}+{\left({\mathrm{x}}_{\mathrm{i}}1\right)}^{2}\right]$  30  [−30, 30]  0 
${\mathrm{f}}_{8}\left(\mathrm{x}\right)={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{ix}}_{\mathrm{i}}^{4}+\mathrm{random}\left(0,1\right)$  30  [−1.28, 1.28]  0 
${\mathrm{f}}_{9}\left(\mathrm{x}\right)={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}\left[{\mathrm{x}}_{\mathrm{i}}^{2}10\mathrm{cos}\left(2{\mathsf{\pi}\mathrm{x}}_{\mathrm{i}}\right)+10\right]$  30  [−5.12, 5.12]  0 
${\mathrm{f}}_{10}\left(\mathrm{x}\right)=20\mathrm{exp}\left(0.2\sqrt{\frac{1}{\mathrm{n}}{\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{x}}_{\mathrm{i}}^{2}}\right)\mathrm{exp}\left(\frac{1}{\mathrm{n}}{\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}\mathrm{cos}(2{\mathsf{\pi}\mathrm{x}}_{\mathrm{i}}\right))+20+\mathrm{e}$  128  [−32.768, 32.768]  0 
${\mathrm{f}}_{11}\left(\mathrm{x}\right)=\frac{1}{4000}{\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{x}}_{\mathrm{i}}^{2}{\displaystyle {\displaystyle \prod}_{\mathrm{i}=1}^{\mathrm{n}}}\mathrm{cos}\left(\frac{{\mathrm{x}}_{\mathrm{i}}}{\sqrt{\mathrm{i}}}\right)+1$  30  [−600, 600]  0 
$\begin{array}{c}{\mathrm{f}}_{12}\left(x\right)=[1+{\left({\mathrm{x}}_{1}+{\mathrm{x}}_{2}+1\right)}^{2}(1914{\mathrm{x}}_{1}+3{\mathrm{x}}_{1}^{2}14{\mathrm{x}}_{2}\\ +6{\mathrm{x}}_{1}{\mathrm{x}}_{2}+3{\mathrm{x}}_{2}^{2})\left]\times \right[30+\\ {\left(2{\mathrm{x}}_{1}3{\mathrm{x}}_{2}\right)}^{2}\times \left(1832{\mathrm{x}}_{1}+12{\mathrm{x}}_{1}^{2}+48{\mathrm{x}}_{2}36{\mathrm{x}}_{1}{\mathrm{x}}_{2}+27{\mathrm{x}}_{2}^{2}\right)]\end{array}$  2  [−2, 2]  3 
${\mathrm{f}}_{13}\left(\mathrm{x}\right)={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{11}}{\left[{\mathrm{a}}_{\mathrm{i}}\frac{{\mathrm{x}}_{1}\left({\mathrm{b}}_{\mathrm{i}}^{2}+{\mathrm{b}}_{\mathrm{i}}{\mathrm{x}}_{2}\right)}{{\mathrm{b}}_{\mathrm{i}}^{2}+{\mathrm{b}}_{\mathrm{i}}{\mathrm{x}}_{3}+{\mathrm{x}}_{4}}\right]}^{2}$  4  [−5, 5]  0.00030 
${\mathrm{f}}_{14}\left(\mathrm{x}\right)=4{\mathrm{x}}_{1}^{2}2.1{\mathrm{x}}_{1}^{4}+\frac{1}{3}{\mathrm{x}}_{1}^{6}+{\mathrm{x}}_{1}{\mathrm{x}}_{2}4{\mathrm{x}}_{2}^{2}+4{\mathrm{x}}_{2}^{4}$  2  [−5, 5]  −1.0316 
${\mathrm{f}}_{15}\left(\mathrm{x}\right)={\left({\mathrm{x}}_{2}\frac{5.1}{4{\mathsf{\pi}}^{2}}{\mathrm{x}}_{1}^{2}+\frac{5}{\mathsf{\pi}}{\mathrm{x}}_{1}6\right)}^{2}+10\left(1\frac{1}{8\mathsf{\pi}}\right){\mathrm{cos}\mathrm{x}}_{1}+10$  2  [−5, 5]  0.398 
F1  F2  F3  F4  F5  
GA–RMSE  0.408266  0.66005  0.457172  69.84426  2.734866 
GABONST–RMSE  0.008912  0.1498  0  15.50201  0 
EATLBO–RMSE  0.241362  0.803974  0.379706  148.6135  0.715572 
BatRMSE  0.3820  0.4033  5.7316  153.3258  0.2902 
BeeRMSE  1.0000  0.5693  9.2615 × 10^{−10}  186.7217  0.2444 
GA–Mean  −0.60383  −1.20717  18.4889  −326.997  −3.7482 
GABONST–Mean  −0.99688  −1.9511  0  −380.897  −6.1295 
EATLBO–Mean  −0.77554  −0.99733  0.27178  −245.971  −5.67757 
BatMean  −0.6215  −1.5889  3.3044  −240.5929  −5.9602 
BeeMean  −9.4481 × 10^{−11}  −1.3024  6.1739 × 10^{−10}  −207.6444  −6.0014 
GA–STD  0.099654  0.290461  0.271562  26.66188  1.358616 
GABONST–STD  0.008434  2.24299 × 10^{−15}  0  11.26751  4.48598 × 10^{−15} 
EATLBO–STD  0.089637  0.00257  0.267856  29.62479  0.56043 
BatSTD  0.0526  0.3463  4.7307  26.4876  0.2381 
BeeSTD  3.1940 × 10^{−11}  0.2769  6.9736 × 10^{−10}  31.9960  0.2103 
F6  F7  F8  F9  F10  
GA–RMSE  115.0308  8.1381 × 10^{3}  0.1612  42.7116  10.9405 
GABONST–RMSE  0  0  2.2336 × 10^{−4}  0  0 
EATLBO–RMSE  4.4872 × 10^{−56}  28.9495  2.7609 × 10^{−4}  205.0804  2.8037 
BatRMSE  1.2039 × 10^{3}  9.0087 × 10^{7}  74.3799  364.5571  20.1364 
BeeRMSE  1.7538 × 10^{3}  2.3143 × 10^{7}  14.6593  141.4336  19.6219 
GA–Mean  113.9390  6.7387 × 10^{3}  0.1464  41.6742  10.9243 
GABONST–Mean  0  0  1.5895 × 10^{−4}  0  0 
EATLBO–Mean  3.3624 × 10^{−56}  28.9495  2.0063 × 10^{−4}  202.5195  2.6162 
BatMean  1.1761 × 10^{3}  8.1520 × 10^{7}  67.4795  362.3866  20.1321 
BeeMean  1.7533 × 10^{3}  2.1898 × 10^{7}  14.3940  140.4960  19.6218 
GA–STD  15.9710  4.6090 × 10^{3}  0.0682  9.4516  39.3751 
GABONST–STD  0  0  1.5852 × 10^{−4}  0  0 
EATLBO–STD  3.0016 × 10^{−56}  0.0175  1.9160 × 10^{−4}  32.6362  1.0182 
BatSTD  259.8600  3.8731 × 10^{7}  31.6047  40.1246  0.4206 
BeeSTD  39.3751  7.5653 × 10^{6}  2.8049  16.4241  0.0496 
F11  F12  F13  F14  F15  
GA–RMSE  2.2342  6.4588 × 10^{−6}  0.0018  2.8284 × 10^{−5}  1.1264 × 10^{−4} 
GABONST–RMSE  0  7.6591 × 10^{−14}  1.4195 × 10^{−4}  2.8453 × 10^{−5}  1.1264 × 10^{−4} 
EATLBO–RMSE  0  9.9516  0.0067  0.0372  0.1170 
BatRMSE  348.4865  21.6007  0.0645  0.8243  0.5704 
BeeRMSE  221.4966  2.5635 × 10^{−9}  2.7909 × 10^{−4}  2.8453 × 10^{−5}  1.1264 × 10^{−4} 
GA–Mean  2.1932  3.000000925712561  0.0017  −1.031628252987515  0.3978873583048 
GABONST–Mean  0  2.999999999999923  4.0025 × 10^{−4}  −1.031628453489878  0.3978873577297 
EATLBO–Mean  0  9.9257  0.0042  −1.0078  0.4496 
BatMean  338.7847  18.6428  0.0464  −0.4807  0.7070 
BeeMean  219.4071  3.000000001676081  5.1854 × 10^{−4}  −1.031628453353341  0.3979 
GA–STD  0.4302  6.4570 × 10^{−6}  0.0011  1.3312 × 10^{−6}  2.3933 × 10^{−9} 
GABONST–STD  0  1.8266 × 10^{−15}  1.0152 × 10^{−4}  5.4942 × 10^{−16}  3.3645 × 10^{−16} 
EATLBO–STD  0  7.2188  0.0055  0.0288  0.1061 
BatSTD  82.4855  15.0473  0.0455  0.6194  0.4843 
BeeSTD  30.6602  1.9593 × 10^{−9}  1.7534 × 10^{−4}  2.0841 × 10^{−10}  1.0905 × 10^{−10} 
Notations  Implications 

N  distinct samples set (X_{i}, t_{i}), where: X_{i} = [x_{i1}, x_{i2}, …, x_{in}]^{T} $\in $ Rn t_{i} = [t_{i1}, t_{i2}, …, t_{im}]^{T} $\in $ Rm 
L  hidden neurons number 
g(x)  activation function, described in Equation (2) [53]. 
W_{i} = [W_{i1}, W_{i2}, …, W_{in}]^{T}  input weights that connect the ith input neurons and the hidden neurons 
${\mathsf{\beta}}_{\mathrm{i}}$ = [${\mathsf{\beta}}_{\mathrm{i}1},{\mathsf{\beta}}_{\mathrm{i}2},\dots ,{\mathsf{\beta}}_{\mathrm{im}}]$^{T}  output weight that connect the ith output neurons and the hidden neurons 
b_{i}  threshold of the ith hidden neurons 
ELM  GABONST  

Parameters  Values  Parameters  Values 
C  Bias and input weight assemble  Iteration numbers  100 
$\mathsf{\beta}$  Output weight matrix  Population size (PS)  50 
Input–weights  −1 to 1  Crossover operation  Arithmetical crossover 
Bias values  0–1  Mutation operation  Uniform mutation 
Number of input nodes  Input attributes  Selection operation  Select a random solution from the top five solutions of the current population 
Number of hidden nodes  650–900, with increment or step of 25  Mean  $\mathrm{Mean}=\frac{{{\displaystyle \sum}}_{\mathrm{i}}^{\mathrm{PS}}\mathrm{f}\left({\mathrm{C}}_{\mathrm{i}}\right)}{\mathrm{PS}}$ 
Output neurones  Class values  Gamma  0.4 
Activation function  Sigmoid 
No  Chanel  Language 

1  Syrian TV  Arabic 
2  British Broadcasting Corporation  English 
3  TV9, TV3, and TV2  Malay 
4  TF1 HD  French 
5  La1, La2, and Real Madrid TV HD  Spanish 
6  Zweites Deutsches Fernsehen  German 
7  Islamic Republic of Iran News Network  Persian 
8  GEO Kahani  Urdu 
Hidden Neuron Numbers  Accuracy  Precision  Recall  FMeasure  GMean 

650  94.37  77.50  77.50  77.50  62.76 
675  94.37  77.50  77.50  77.50  62.50 
700  93.75  75.00  75.00  75.00  59.38 
725  94.37  77.50  77.50  77.50  62.81 
750  95.63  82.50  82.50  82.50  69.64 
775  95.00  80.00  80.00  80.00  66.11 
800  95.63  82.50  82.50  82.50  69.64 
825  95.00  80.00  80.00  80.00  66.16 
850  95.00  80.00  80.00  80.00  66.16 
875  96.25  85.00  85.00  85.00  73.41 
900  95.00  80.00  80.00  80.00  66.20 
Hidden Neuron Numbers  Accuracy  Precision  Recall  FMeasure  GMean 

650  92.50  70.00  70.00  70.00  53.40 
675  98.12  92.50  92.50  92.50  85.85 
700  98.12  92.50  92.50  92.50  85.85 
725  99.38  97.50  97.50  97.50  95.06 
750  97.50  90.00  90.00  90.00  81.56 
775  98.75  95.00  95.00  95.00  90.25 
800  99.38  97.50  97.50  97.50  95.06 
825  99.38  97.50  97.50  97.50  95.06 
850  99.38  97.50  97.50  97.50  95.06 
875  99.38  97.50  97.50  97.50  95.06 
900  98.12  92.50  92.50  92.50  85.85 
Hidden Neuron Numbers  Accuracy  Precision  Recall  FMeasure  GMean 

650  89.44  55.00  55.00  55.00  31.49 
675  90.02  57.50  57.50  57.50  39.85 
700  90.55  52.50  52.50  52.50  27.67 
725  88.88  47.50  47.50  47.50  27.00 
750  89.44  42.50  42.50  42.50  20.34 
775  89.16  45.00  45.00  45.00  22.80 
800  90.00  55.00  55.00  55.00  36.13 
825  89.72  55.00  55.00  55.00  30.03 
850  88.88  50.00  50.00  50.00  25.95 
875  90.55  55.00  55.00  55.00  30.36 
900  90.55  52.50  52.50  52.50  29.51 
Hidden Neuron Numbers  Accuracy  Precision  Recall  FMeasure  GMean 

650  84.38  37.50  37.50  37.50  25.36 
675  87.50  50.00  50.00  50.00  34.19 
700  85.00  40.00  40.00  40.00  27.13 
725  87.50  50.00  50.00  50.00  33.69 
750  86.88  47.50  47.50  47.50  32.21 
775  88.75  55.00  55.00  55.00  38.43 
800  86.25  45.00  45.00  45.00  30.34 
825  88.75  55.00  55.00  55.00  38.34 
850  86.88  47.50  47.50  47.50  31.86 
875  89.38  57.50  57.50  57.50  40.53 
900  86.88  47.50  47.50  47.50  31.86 
Hidden Neuron Numbers  Accuracy  Precision  Recall  FMeasure  GMean 

650  88.12  52.50  52.50  52.50  36.00 
675  89.38  57.50  57.50  57.50  40.63 
700  88.75  55.00  55.00  55.00  38.40 
725  92.50  70.00  70.00  70.00  53.44 
750  90.00  60.00  60.00  60.00  42.88 
775  88.75  55.00  55.00  55.00  38.16 
800  90.63  62.50  62.50  62.50  45.26 
825  89.38  57.50  57.50  57.50  40.49 
850  88.75  55.00  55.00  55.00  38.09 
875  88.12  52.50  52.50  52.50  36.24 
900  90.00  60.00  60.00  60.00  42.65 
Number of Hidden Neurons  Accuracy  Precision  Recall  FMeasure  GMean 

650  96.88  87.50  87.50  87.50  77.43 
675  96.25  85.00  85.00  85.00  73.19 
700  95.00  80.00  80.00  80.00  66.16 
725  97.50  90.00  90.00  90.00  81.61 
750  93.75  75.00  75.00  75.00  59.11 
775  97.50  90.00  90.00  90.00  81.56 
800  96.88  87.50  87.50  87.50  77.43 
825  92.50  70.00  70.00  70.00  53.40 
850  95.63  82.50  82.50  82.50  69.74 
875  96.88  87.50  87.50  87.50  77.27 
900  95.00  80.00  80.00  80.00  66.16 
Number of Hidden Neurons  Accuracy  Precision  Recall  FMeasure  GMean 

650  93.13  72.50  72.50  72.50  56.20 
675  94.37  77.50  77.50  77.50  62.58 
700  93.13  72.50  72.50  72.50  56.16 
725  93.75  75.00  75.00  75.00  59.46 
750  91.87  67.50  67.50  67.50  50.44 
775  92.50  70.00  70.00  70.00  53.12 
800  93.13  72.50  72.50  72.50  56.37 
825  93.75  75.00  75.00  75.00  59.38 
850  93.13  72.50  72.50  72.50  56.20 
875  93.75  75.00  75.00  75.00  59.33 
900  92.50  70.00  70.00  70.00  53.09 
Number of Hidden Neurons  Accuracy  Precision  Recall  FMeasure  GMean 

650  93.75  75.00  75.00  75.00  59.54 
675  93.13  72.50  72.50  72.50  56.37 
700  92.50  70.00  70.00  70.00  53.56 
725  93.75  75.00  75.00  75.00  59.32 
750  91.87  67.50  67.50  67.50  50.24 
775  93.75  75.00  75.00  75.00  59.50 
800  93.13  72.50  72.50  72.50  56.16 
825  93.75  75.00  75.00  75.00  59.37 
850  93.13  72.50  72.50  72.50  56.21 
875  95.00  80.00  80.00  80.00  66.25 
900  92.50  70.00  70.00  70.00  53.29 
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Albadr, M.A.; Tiun, S.; Ayob, M.; ALDhief, F. Genetic Algorithm Based on Natural Selection Theory for Optimization Problems. Symmetry 2020, 12, 1758. https://doi.org/10.3390/sym12111758
Albadr MA, Tiun S, Ayob M, ALDhief F. Genetic Algorithm Based on Natural Selection Theory for Optimization Problems. Symmetry. 2020; 12(11):1758. https://doi.org/10.3390/sym12111758
Chicago/Turabian StyleAlbadr, Musatafa Abbas, Sabrina Tiun, Masri Ayob, and Fahad ALDhief. 2020. "Genetic Algorithm Based on Natural Selection Theory for Optimization Problems" Symmetry 12, no. 11: 1758. https://doi.org/10.3390/sym12111758