Optimizing Extremum Values using Neural Network Genetic Algorithm
For unknown nonlinear functions, accurately finding extremum values solely through input-output data is challenging. This problem can be solved by combining neural networks with genetic algorithms, leveraging neural networks' nonlinear fitting capabilities and genetic algorithms' nonlinear optimization abilities to locate function extrema. This article demonstrates how to optimize extremum values for nonlinear functions using neural network genetic algorithms, with implementation details including network architecture design and genetic operation parameters.