Evolutionary Computation and its Applications

Evolutionary Computation and its Applications

This course is part of Applied AI for Engineers and Scientists: Foundations Specialization

Instructor: Bo Liu

1,640 already enrolled

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3 modules

Gain insight into a topic and learn the fundamentals.

32 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

32 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Explain core optimization concepts used in science and engineering.
Apply genetic algorithms and particle swarm optimization in MATLAB to science and engineering optimization problems.

Skills you'll gain

Details to know

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Assessments

3 assignments

Taught in English

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This course is part of the Applied AI for Engineers and Scientists: Foundations Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 3 modules in this course

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.

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:

What's included

12 videos10 readings1 assignment

12 videosTotal 75 minutes

Welcome to the specialization - Applied AI for Engineers and Scientists: Foundations6 minutes
Introduction to Module 4's Study1 minute
Introduction to Optimization: Concepts and Applications10 minutes
Introduction to Optimization: Further Applications6 minutes
Optimization Objectives and Constraints: Objectives and Constraints6 minutes
Optimization Objectives and Constraints: Multiple Objectives9 minutes
Global and Local Optimization: Concepts10 minutes
Formulate Engineering Problems to Optimization Problems: Procedures and A Case Study11 minutes
Case Studies (1)4 minutes
Case Studies (2)7 minutes
Case Studies (3)3 minutes
Case Studies (4)1 minute

10 readingsTotal 85 minutes

Specialization and Course Structure10 minutes
Specialization Sample Certificate10 minutes
Why Optimization Matters in Engineering and Science5 minutes
Optimization and Evolutionary Computation in AI5 minutes
Materials about Optimization10 minutes
Materials about Optimization Objectives and Constraints10 minutes
Materials about Global and Local Optimization10 minutes
Materials about Optimization Problem Formulation10 minutes
Materials about Case Studies10 minutes
Module 4 Recap5 minutes

1 assignmentTotal 30 minutes

Module 4 Quiz30 minutes

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:

What's included

9 videos12 readings1 assignment1 app item

9 videosTotal 60 minutes

Introduction to Module 5's Study1 minute
Genetic Algorithm: A First Introduction7 minutes
Genetic Algorithm: A Demo8 minutes
Genetic Algorithms: GA Representation, Initialization and Selection11 minutes
Genetic Algorithms: GA Crossover and Mutation8 minutes
Genetic Algorithms: GA Stopping Criterion and Summary6 minutes
Use GA via MATLAB: GA Function7 minutes
Use GA via MATLAB: Employ the Function7 minutes
Use GA via MATLAB: Case Studies6 minutes

12 readingsTotal 80 minutes

How to Access MATLAB Online5 minutes
Materials on Genetic Algorithms10 minutes
How Genetic Algorithms Solve Optimization Problems5 minutes
Materials on Genetic Algorithms10 minutes
How GA Operators Work Together5 minutes
Materials on Using Genetic Algorithms10 minutes
Using Genetic Algorithms Effectively in MATLAB5 minutes
Materials on Case Studies10 minutes
Solving Real Engineering Problems with Genetic Algorithms5 minutes
Objective function for Assignment 55 minutes
Applying Genetic Algorithms to a Real Engineering Design Problem5 minutes
Module 5 Recap5 minutes

1 assignmentTotal 30 minutes

Module 5 Quiz30 minutes

1 app itemTotal 30 minutes

Module 5 Assignment30 minutes

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:

What's included

7 videos7 readings1 assignment1 app item

7 videosTotal 43 minutes

Introduction to Module 6's Study1 minute
Particle Swarm Optimization: A First Introduction7 minutes
Particle Swarm Optimization: Operators7 minutes
Particle Swarm Optimization: Workflow Demo12 minutes
Use PSO via MATLAB: PSO Function7 minutes
Use PSO via MATLAB: Employ the Function4 minutes
Use PSO via MATLAB: Case Studies6 minutes

7 readingsTotal 50 minutes

Materials on Particle Swarm Optimization10 minutes
Understanding Particle Swarm Optimization Mechanics5 minutes
Materials on Using Particle Swarm Optimization10 minutes
Materials on Using Particle Swarm Optimization10 minutes
Applying PSO to a Real Engineering System5 minutes
Objective function for Assignment 65 minutes
Module 6 Recap5 minutes

1 assignmentTotal 30 minutes

Module 6 Quiz30 minutes

1 app itemTotal 30 minutes

Module 6 Assignment30 minutes

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Instructor

Bo Liu

University of Glasgow

3 Courses4,061 learners

Offered by

University of Glasgow

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Reviewed on Nov 20, 2025

The course’s emphasis on robustness and adaptability in evolutionary solutions has equipped me to handle noisy data and dynamic environments in real-time control systems.

Reviewed on Nov 20, 2025

The instructor’s expertise in evolutionary multi-objective optimization (EMO) helped me tackle complex trade-offs in sustainable urban planning projects.

Reviewed on Nov 9, 2025

Amazing lectures, I have learnt a lot. Many thanks.

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