(a) Comparison between algorithms divided into: Class 1, success rates; Class 2, average CPU times and Class 3, average objective function values (ϕ(m) × 102). (b) Objective function values for successful models.</p
Statistical analysis and comparison of the optimum results obtained by three algorithms.</p
Population size = 100 and Generation = 300. (a) Range, (b) Average Tolerance, (c) Average Hamming Di...
Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the use...
(a) Comparison between algorithms divided into: Class 1, success rates; Class 2, average CPU times a...
(a) Comparison between algorithms divided into: Class 1, success rates; Class 2, average CPU times a...
Results of the success rate (sr) and average execution time (rt) of each optimization algorithm.</p
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
<p>Comparison of the average classification accuracies of different algorithms for different numbers...
Comparative statistical performance of various algorithms in minimizing the F cost function.</p
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
<p>The comparison of computational efficiency of each algorithm on the test functions.</p
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>(a) The FNR and FPR Comparison of algorithms. And (b) the Performance Comparison of Algorithms.</...
Performance comparison of recent and effective algorithms for minimizing error-based cost functions....
Comparative model performance: Low complexity choices, base rate of comparison: 50%.</p
Statistical analysis and comparison of the optimum results obtained by three algorithms.</p
Population size = 100 and Generation = 300. (a) Range, (b) Average Tolerance, (c) Average Hamming Di...
Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the use...
(a) Comparison between algorithms divided into: Class 1, success rates; Class 2, average CPU times a...
(a) Comparison between algorithms divided into: Class 1, success rates; Class 2, average CPU times a...
Results of the success rate (sr) and average execution time (rt) of each optimization algorithm.</p
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
<p>Comparison of the average classification accuracies of different algorithms for different numbers...
Comparative statistical performance of various algorithms in minimizing the F cost function.</p
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
<p>The comparison of computational efficiency of each algorithm on the test functions.</p
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>(a) The FNR and FPR Comparison of algorithms. And (b) the Performance Comparison of Algorithms.</...
Performance comparison of recent and effective algorithms for minimizing error-based cost functions....
Comparative model performance: Low complexity choices, base rate of comparison: 50%.</p
Statistical analysis and comparison of the optimum results obtained by three algorithms.</p
Population size = 100 and Generation = 300. (a) Range, (b) Average Tolerance, (c) Average Hamming Di...
Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the use...