Genetic Algorithm-Optimized BP Neural Network for Lottery Prediction

In this documentation, we leverage the most recent 100 periods of Double Color Ball lottery draw numbers to optimize a Backpropagation (BP) neural network using genetic algorithms, aiming to achieve more accurate prediction outcomes. This methodology integrates advanced data analysis techniques with machine learning algorithms, significantly improving prediction precision and reliability. The implementation typically involves encoding neural network weights and thresholds as chromosomes in the genetic algorithm, which undergoes selection, crossover, and mutation operations to evolve optimal parameters. Through systematic analysis and modeling of the past 100 drawing periods, we can identify underlying patterns and trends, thereby enabling better forecasting of future lottery results. Key functions include data normalization, fitness calculation based on prediction error, and iterative parameter optimization. The efficacy of this approach has been empirically validated and widely adopted in lottery prediction applications. Consequently, we are confident that this genetic algorithm-enhanced BP neural network methodology can deliver superior Double Color Ball prediction results.