Figure 1.
![An example of CNN architecture [10]](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/6471f610215d2f6c89db6db8/j_ijanmc-2021-024_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIA6AP2G7AKDAF3P646%2F20260119%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20260119T143239Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEMz%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaDGV1LWNlbnRyYWwtMSJIMEYCIQCx9iKYJbWddEAw%2BNFurMOcrxJGbDx5VK6X2D9kcRyJHgIhAMlGnc9MJGy7jlLHD9opUz27dPflBs7OVRInFyVTT7y6KsUFCJX%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQAhoMOTYzMTM0Mjg5OTQwIgz2enh7ghrd%2B%2BgvrTwqmQXG4c4Zacfnu0wefLTE7agW2bbjwrokhzENxfowDDdIvnt%2BWZABTv%2FyjE1r3PdHx%2F3jLgDBWrDCGOWWRIEljrV5%2FE4AUgDBY%2FmagGUbKXGcyV3XLbHMurb8579pQNJs46S0Y09%2Fz5j5sAGi9sBfWa0ce%2BwQI3q%2BTkrPJ7s0giti%2BIdizY5NBNBz4BJ2lIc8FKWX6q6J7ObjpU4Q45lYSEUiqjck6L1CkYjhQzQJKrlMO1OU8QZzP%2F%2FTf75N84WS0Qg0fDcSOdgWAGgzHHXK01HV0FckIWNSq%2BOYHoqgCaPcrpAmrFuoH5A5DUmWRs%2B4H2d6jmSwJX52qMY83hoyOH5P5LAMou%2F39ArWE58ZTe9pPANF1ECFkffxzczEKuZ4IUDi2pExg1OCrZ%2FN%2B%2BiBgE9rnxJEZL6Iyq5vQMm1nvRpGb%2FKVC1OCyc4p7ojZxelW%2FIGZlQnQ7812Z9buN3STU5M1ZKuQ3QurWnW1lBHyqtuRsfQEqS1wbW3VE6eGSfABvJzBiK7%2FEebhzZsV7DIUgK6b%2FoxTMUQyEOOj%2BKdlrzdFQAUyNBV7q7LsChfyb%2FAQceG64bHvSl5o9gz9%2B%2B%2BfPcmECGp7B1bfy7QZEId07NBtjgF9ePmWWGA3B%2FdvE6rCdqIgaQSTfKLdbQjLIZKYfKDoHenj8lSqfTgbTRZ4LRb2lMEagndFW0T06wZQXSh0jjAlTaOqmUTSoZdjMjT1qYmGw4LupJddDgddCASfckemaCIvaDGa4oyBO8xREYLBmoi%2BvBtt7Yjp9mulW9rJz%2FjjL4RI5GQoL%2F5duRvrMs43qUUQAZIiVCYUdVcqoZyx4XOsYfqENkkSWoB9CYXPiK%2Fcnn%2FnWUdAoZ6B76LpHam6aCt7Ld5JvDdGjDhwrjLBjqwAcHCCdieW5MLo%2By3LmvPMX%2FuRDiYhh2CxVcbemfpQZ9nudi2TQD5ig9pDqNmmBCCwwATOKlYkDFog%2BlTPQbAxQysIH1nECTNuV%2B8oeGhCRoEcvvxk5ZUnC7CPwyDe%2F4LNObmntAKIx6avirXSCyfJmnuNeG5iQUmNvSo7LdRCQ%2FbsAPu%2B0kaj2XkWvl%2BYJr8QAUKRf7i7cFT2OCJoZEK5iJNkq2QjcTK32pUtNU89QK7&X-Amz-Signature=6fde3d57e742875ddb37a628ecfd57ddaee42e2f8f648ebf58c04c543f52b18f&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Figure 6.
Figure 7.
Figure 8.
Parameters of the genetic operations
| Parameters | Value |
|---|---|
| Tournament selection size | 2 |
| Crossover Probability | 50% |
| Mutation probability | 80% |
| Genes Mutated | 10% |
Highest fitness values obtained during each of the 10 experiments
| Exp. No. | Highest Fitness Value |
|---|---|
| 1 | 0.984499992943 |
| 2 | 0.973899998105 |
| 3 | 0.988800008184 |
| 4 | 0.991900001359 |
| 5 | 0.947799991965 |
| 6 | 0.949000005102 |
| 7 | 0.983099997652 |
| 8 | 0.979799999475 |
| 9 | 0.956399999567 |
| 10 | 0.972350000068 |
The various hyper parameters in CNN with their ranges
| Hyper parameter | Range |
|---|---|
| No. of Epoch | (0 – 127) |
| Batch Size | (0 – 256) |
| No. of Convolution Layers | (0 – 8) |
| No. of Filters at each Convo layer | (0 – 64) |
| Convo Filter Size at each Convo layer | (0 – 8) |
| Activations used at each Convo layer | (sigmoid, tanh, relu, linear) |
| Maxpool layer after each Convo layer | (true, false) |
| Maxpool Pool Size for each Maxpool layer | (0 – 8) |
| No. of Feed-Forward Hidden Layers | (0 – 8) |
| No. of Feed-Forward Hidden Neurons at each layer | (0 – 64) |
| Activations used at each Feed-Forward layer | (sigmoid, tanh, softmax, relu) |
| Optimizer | (Adagrad, Adadelta, RMS, SGD) |