Evolutionary Computation and its Applications

Overview

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This course introduces the foundations of optimization and shows how AI can be applied to real-world science and engineering optimization problems. You will learn about evolutionary computation, a branch of AI for optimization. You will explore two widely used AI-based optimization techniques: genetic algorithms and particle swarm optimization. Along the way, you will learn how these methods work, when to use them, and how to implement them in MATLAB toolboxes to solve design and decision-making problems. The course combines core concepts with practical science and engineering case studies, helping you move from theory to application. By the end of the course, you will be able to define optimization problems and use AI methods to obtain solutions in realistic contexts. In partnership with MathWorks, enrolled learners receive access to MATLAB for the duration of the course.

Syllabus

Fundamental Concepts in Optimization

One of the most important applications of AI in science and engineering is optimization. This module introduces fundamental concepts in optimization. After learning this module, students will be able to:

Genetic Algorithm

This module introduces genetic algorithm (GA), which is a famous optimization method inspired by the intelligence of the evolution process. After learning this module, students will be able to:

Particle Swarm Optimization

This module introduces particle swarm optimization (PSO). Swarm intelligence has attracted much attention in the AI field and PSO is a renowned global optimization algorithm based on swarm intelligence. After learning this module, students will be able to: