I am trying to do classification using NEAT-python for the first time, and I am having difficulty getting the accuracy rate. I tried the same problem with an ANN and was able to get a good accuracy rate (96%+), but NEAT-Python gives barely 40%.
Here's how I set up:
Problem: Train 100 probability values to predict classification (1-10)
Input and output setup: inputs = number of input shape (100 values of prob), and output is 10 values of probability assoc with 10 classes.
Activation: I applied ReLU for feedforward, then applied softmax
- Fitness function: I used the loglikelihood. I was unsure about how to set up the fitness function. I also used mean accuracy rate in the genome. Both gave similar results.
In terms of hyperparameters, I am trying various values and haven't had any luck with it. Today I am trying with an increase in population size and generations. I have another feature input that can be used.
Are there any resources that discuss how to handle mixed data for NEAT?
Any help is greatly appreciated.