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=ASIA6AP2G7AKFMEVFLVP%2F20260310%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20260310T171256Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEIH%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaDGV1LWNlbnRyYWwtMSJHMEUCIBhuMximnF2CrexJePK2l1%2BLB5bHT%2BtWrGn0N05%2F7WOeAiEA0lCt7v%2F1tOHx1CpDOqeNhEXRIkCaaBSEaPof76gyJ1UqvQUIShACGgw5NjMxMzQyODk5NDAiDDoUPo9P5hh3x3fMNiqaBU%2FASTqoFyi4nZWsIKx%2B89qcmd56iLiJO%2FDCBAi3UqXAuDjWt0GDO8c7bL%2Fxcc%2FOQZIXq2W5kbKaUWpR2G%2F12QHVyRya3A8znYGqNPLflOykEXZ4scYi%2BhVEzi6fW8lRdRxk3MEr2CyioRoyIHU7vkGJ7%2FG4%2Be3PgBvE3UwtFtEGorhdUfQf4KIjjeWRqy5fhBji0omew4UcW39Mi1G8GHWjZ7dbl25yES3d8BriINyDB6R5xW2cI0a4G3AtIKq0pTnNHb%2BZ%2BBpoYR%2Fel7F8HzCE3gFeh1QM6teVakAaS1L9DUwWV%2Bvlg%2FLvcmjr4781WFl4Lia%2FzyYLIFGv27L9NLTtp68y9Mge%2FhWh92UPzrBN6dD%2B0kH0Opc5MBnxyVR1CTXxV0sgFy1jJCg7Ua4fasjhUzrp5PvgF8empVBnIfmAzzT1dIn4jG7UYdfOQOpj2M1xjvrjezrYZ8zjvL5HSySW2iWY9m1dQ1SSGdNXhYu56yAPlZsnvTKmsw7VbQ45gTihypOHV99ptz%2BSlQ8BEU9C9r40proKETcqhhQRzFxZXbdrL9O4n%2B3WOIwgi%2Fdzus%2F30SCYt351%2F4n0SGGwa3q8TXK%2Bps9V%2FpDbYgoaDYa6Pw0mHIOS14UeRbTkf7b4pqicwjX58I8jB7nY9a6yd01BjvJ0AlC8D8YnqU617zct5cZjs6dKqTYcmYapVOGiwTeESCJQ6B9OUviXXEjMzrp%2BmLYe5oUkr%2BmbQAnwxyGYtXe3axpNf1eGlqXFPjjGk0havLcVSGWv%2BT8Kzelr1SXMCqUX84VjDgMGdj0ukmwR0MUU6punEqJGc67rSu4uirwtkfCCVlPrbOdciVVs92ZC6BgeOksk3YIikTaDFtZyXZkKW6nVCsmKgjDwjcHNBjqxAYRkVdNr9%2B%2BVMhBUrG0NUvhbGVmioVklWR8OIwlWtTmouXME4cmLSYYj7juhgY9vGC2N4yMVtKAg%2FHiuHyH1r4pnhm9vg%2BZpTjmF37dXxnAn5aV%2BtbsAMv5EBfJkv5XzCaHW3rzRkfFqvCmlHH56MXItM%2Fld%2FbcsF1dQYb%2F9KirUUqWU3BK7eH8FnV4KNrIAm2cm3hi%2FP4Fhl4xqhmH2jQcHlvXPrtRQq4S1NOg79%2BRQ%2BA%3D%3D&X-Amz-Signature=1c557e4c6f27705b006856936949f41fcfbba75b2d5fb32fa8cddbb11953be31&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) |