Genetic Algorithm Optimization of BP Neural Networks

Genetic Algorithms represent a parallel stochastic optimization search method that simulates natural genetic mechanisms and biological evolution principles. Originally proposed in 1962 by Professor Holland at the University of Michigan, this approach optimizes parameters within encoded population strings by applying fitness function evaluation alongside genetic operations including selection, crossover, and mutation. In typical implementations, the algorithm initializes a population of candidate solutions, evaluates each individual's fitness score, then performs selection (e.g., tournament or roulette wheel selection), crossover (single-point or multi-point recombination), and mutation (bit-flip or Gaussian perturbation) operations. High-fitness individuals are preserved while low-performance solutions are eliminated, generating new populations that inherit parental characteristics while exhibiting improved overall quality. This evolutionary process iterates through generations until meeting termination conditions such as maximum iterations or convergence thresholds, with common implementation features including elitism preservation and adaptive parameter tuning.