Can random optimization algorithms replace backpropagation for training neural networks? This analysis compares three random optimization approaches against traditional backpropagation on the Apple Quality dataset.

The Algorithms

Random Hill Climbing (RHC)

Starts with a random solution and iteratively improves it by making small changes and occasionally restarting. Simple but prone to getting stuck in local optima.

Simulated Annealing (SA)

Explores neighboring solutions, accepting worse outcomes with decreasing probability over time. This balances exploration (trying new regions) with exploitation (refining good solutions). The “temperature” schedule controls this tradeoff.

Genetic Algorithm (GA)

Works with a population of potential solutions, applying genetic operations — selection, crossover, and mutation — across generations. The most biologically inspired approach.

Why Consider Alternatives to Backpropagation?

Three known problems with backpropagation:

  1. Local optima entrapment in deep networks
  2. Slow convergence rates
  3. Vanishing and exploding gradients during training

Results

Method Validation Recall Training Time
Backpropagation 0.87 17 seconds
RHC 0.57 22 seconds
SA 0.53 11 seconds
GA 0.64 55,210 seconds

Conclusion

For datasets with clear gradients, backpropagation wins decisively — best accuracy in the least time. The Genetic Algorithm took over 15 hours and still fell well short. These random optimization methods have their place (combinatorial optimization, non-differentiable objectives), but neural network training isn’t it.