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=ASIA6AP2G7AKNYWPXYEY%2F20260310%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20260310T150146Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHsaDGV1LWNlbnRyYWwtMSJIMEYCIQClI2hSXo347tNB0AZn9YAp4oa8374nAkqppFAFCufyVgIhAIyuVJldNdRC1OmGU7DTQuXfOEtXVp79x5n1z20zMhC3KrwFCEQQAhoMOTYzMTM0Mjg5OTQwIgxARI2258BOFo4ECrMqmQUNU%2Fnw3L%2FfBz1d59Tla9caS9nYRDfIQZ3ROLtS8E%2FiIf19AocqxOaYMoJ1kZXXeAmEaNiSeQpOLS94Ebtk0Y7%2BKsWQv41R6rHn%2Fp0CF3WQ%2Brd6j9NWLvFIGs5nPvSu%2FTJzhNkUiT1QRnKaLTffZyirwTbSgPbXD7e%2FYrOciNYb%2FqatKAqTHdXiDxxshy6bwM6FGyZ65C5LA8DIJO%2FIjrI4fldTjx%2FVfZp49DEyapNODlwHqFXBN2AnwXH4qM9en5RvlnZj6h%2B7C3kbtGNODEWgd2SVjD6ORn4VDuiLYWDDaGRRmWyAKr7g%2FtuRz8jLsEzjguuns%2BeWYxAYt8EFrsU9IZb1N7jqyXzn4WhSBWqMz4%2F5yDVtVLK9J8%2B5cMZ7Q68Zan2pQZvtfsyf4HMDKrMQVlI3SID%2Fy%2Fy9UC5tqp54P1xesQIFoSrCnfoj37XyZLVhhVfFHhZI7kia1RAHj%2FI4eIDHuWQ1piTjU5lwbQqauw8rEqd3wEj87LskuTl%2FcgjQ3Fr8ipLPzltJGorsMZZBh2seesbCEvXDRXKrLYK63PL95v%2FHWnydzH6wiqmYc%2Bn%2B2ToHmyyBIfeuj4wBMAH%2FsF1jDJWBUnN4wIUq2UtQcTecLfcJeadldkW5CsQDaMYdlM7qimszAMSafQ3ok%2F%2BHh7faWO07o1JpISNso5VHV4qpVtp6fotwu2r9EJzxRJ9G2ta%2Buu%2BhSzVGypjAkQ5n4AlbrJEGqVPr%2FnfBOu1w3u2eGDOkjzaf2S3Be0tH%2B0tVf5VD1HnjCZdYjDmNk7Q78l%2BVrnH59JxQ%2Fz5L5TIXvbWrdIYqnubyLNxhE5QG2fI5ggt1XY4ruBBaaMzPOsdvmHhdwrJdwdTrs0Tyqq3t1YjnhkqFKKX6UTCg77%2FNBjqwAc29Xq94EzMT3WQLS67ZIud6IHo6xp4BIjpH6Wn%2BPu5nou7SoIwIt%2FcKTsXduk%2F1CWYlqDSjE9mDrCAEpIKekQGj3t%2BoOCZlU6gLzZCbAd1TXf7vdUYLCGmcD0AzlqCCGNOpgMZqbdHBjCcFb%2FduzjIyG1yzpSs5IB3gFb9B%2BZPtwoReLI9sTZNBw%2B14ZAZxAgbjjducTi%2FhgbAsc47kwNXUApoH4IIJuf0k3KafDREX&X-Amz-Signature=18c7abb9056a41088193bb345af59cc8c2a348628a918cf3114c791b64a563d4&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
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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) |