This repository contains the Whitebox implementation of Ant Colony Optimization, Cultural Algorithm, Genetic Algorithm, and Particle Swarm Optimization for optimizing neural network weights. These algorithms can be used to train neural networks in a more efficient manner, by finding the optimal values for the weights.
Ant Colony Optimization
Cultural Algorithm
Genetic Algorithm
Particle Swarm Optimization
Each algorithm has been implemented in a separate Python file, which contains the necessary functions and parameters for running the algorithm.
The neural network used in this repository has the following architecture:
Input layer: 12 neurons (for 12 features)
Hidden layer: 10 neurons
Output layer: 1 neuron
| Algorithm | Test Accuracy |
|---|---|
| Ant Colony Optimisation | 73.39% |
| Cultural Algorithm | 68.75% |
| Genetic Algorithm | 76.5% |
| Particle Swarm Optimisation | 88.54% |
| Hidden Layer Size | Generations | Chromosomes | Mating Parents | Crossover Rate | Mutation Rate | Train Accuracy | Test Accuracy |
|---|---|---|---|---|---|---|---|
| 7 | 25 | 40 | 10 | 0.80 | 0.4 | 0.7357 | 0.7396 |
| 10 | 25 | 40 | 10 | 0.80 | 0.4 | 0.6745 | 0.6510 |
| 10 | 30 | 40 | 10 | 0.80 | 0.4 | 0.7435 | 0.7448 |
| 10 | 20 | 30 | 10 | 0.85 | 0.3 | 0.8398 | 0.8490 |
Particle Swarm optimisation gave the best results in comparison with the other three evolutionary algorithms
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