Stochastic Optimization and Genetic Minimizers in MATLAB

When traditional gradient-based optimization methods fail on complex, non-linear, or discontinuous chemical engineering problems, stochastic optimization offers a powerful path forward. Understanding how to model uncertainty and navigate rugged multi-variable landscapes is an essential skill for modern process and research engineers. This course provides a clear, text-based guide to implementing genetic minimizers and probability-based simulations from scratch. You will transition from theoretical mathematics to writing functional optimization scripts that solve real-world engineering challenges. What you will learn: Understand the foundational principles of probability theory and stochastic simulation in engineering; Implement genetic algorithms including selection, crossover, mutation, and elitism; Write clean, modular MATLAB code to minimize multi-variable objective functions; Apply stochastic methods to chemical engineering design, reaction kinetics, and process optimization; Analyze algorithm convergence and tune hyperparameters for robust optimization performance. We begin with core mathematical definitions and the fundamentals of probability, ensuring you have a solid theoretical foundation. From there, we walk step-by-step through the mechanics of genetic algorithms, translating these concepts into practical MATLAB implementations with modern coding best practices. This course is designed for undergraduate students, researchers, and practicing engineers who have a basic familiarity with MATLAB and want to master stochastic numerical methods. Start learning today and unlock new ways to solve complex engineering optimization problems.