Optimizing Extremum Values using Neural Network Genetic Algorithm

For unknown nonlinear functions, accurately determining extremum values using only input-output data presents significant challenges. However, combining neural networks with genetic algorithms provides an effective solution approach. Neural networks possess strong nonlinear fitting capabilities, while genetic algorithms excel at nonlinear optimization - the integration of these two methods enables more precise identification of function extrema. This article will demonstrate how to optimize extremum values for nonlinear functions using neural network genetic algorithms, making the process clearer and more accessible. The implementation typically involves designing a neural network architecture (including hidden layer configuration and activation functions) to approximate the target function, followed by applying genetic algorithms (with carefully selected crossover and mutation parameters) to optimize the network's weight parameters for extremum search. We will provide concrete examples of common nonlinear functions to help readers better understand the practical application of this methodology. The code implementation would typically involve defining the objective function, configuring the neural network's topology, setting genetic algorithm parameters (population size, generations, selection criteria), and establishing the fitness evaluation mechanism that combines both techniques.