Application of MATLAB Genetic Algorithm Toolbox for Optimization Problem Solving

In this paper, we explore the application of MATLAB's Genetic Algorithm Toolbox for solving optimization problems. Genetic algorithms are optimization techniques that simulate natural selection and genetic mechanisms. By mimicking evolutionary operations such as genetic variation, selection, and crossover, genetic algorithms effectively discover optimal solutions to complex problems. Within MATLAB, we leverage functions and methods provided by the Genetic Algorithm Toolbox to achieve this objective through proper code implementation. Key implementation steps include defining objective functions using anonymous functions or separate m-files, setting constraint conditions through linear/nonlinear constraint functions, and configuring algorithm parameters like population size and crossover rates using optimoptions. The paper provides detailed methodologies and step-by-step procedures for applying the Genetic Algorithm Toolbox in MATLAB, including practical case studies with real-world optimization scenarios and comprehensive result analyses. Implementation highlights cover the use of ga() as the main optimization function, fitness function design techniques, and parameter tuning strategies for improved convergence.