Splitting datasets spatially and training specialized neural networks in parallel using evolutionary algorithms can outperform traditional gradient-based training while being faster and more scalable.
This paper proposes two new neural network training methods (MC-PSO and MC-APSO) that split datasets into spatial subsets and train small specialized neural networks on each subset using particle swarm optimization. During prediction, only relevant networks contribute to the final answer, improving both accuracy and speed compared to existing methods.