Function Optimization Using Genetic Algorithms and Nonlinear Programming

This article introduces a hybrid function optimization approach combining genetic algorithms and nonlinear programming. This methodology leverages the global search capabilities of genetic algorithms for broad exploration and the precision of nonlinear programming for local refinement, creating an efficient optimization framework. We will detail the algorithmic workflow including population initialization, fitness evaluation, selection mechanisms (e.g., tournament selection), crossover operations (using techniques like simulated binary crossover), and mutation strategies. The nonlinear programming component typically employs gradient-based methods (such as sequential quadratic programming) for fine-tuning solutions. Accompanying this explanation is a comprehensive tutorial demonstrating practical implementation with MATLAB/Python code examples showcasing key functions like fitness calculation and constraint handling. Due to file size restrictions, tutorial images may lack clarity. For high-definition tutorial materials with complete code annotations, please contact me for dedicated assistance.