Back to Evolutionary Computation and its Applications
University of Glasgow

Evolutionary Computation and its Applications

One of the most important applications of AI in engineering is optimization. Optimization is almost needed everywhere in science and engineering. Compared with traditional mathematical optimization techniques, evolutionary computation, which is a branch of AI, is attracting much attention. After taking this course, students will be able to understand how evolutionary computation works and fluently use AI-based optimization techniques to solve engineering optimization problems via MATLAB. This course introduces fundamental concepts in optimization and the working principles of genetic algorithm and particle swarm optimization in a comprehensive and understandable way. Case studies from real-world engineering are provided, making sure students have the ability to apply what they have learned in real practice. In partnership with MathWorks, enrolled students have access to MATLAB for the duration of the course.

Status: Engineering
Status: Engineering Analysis
BeginnerCourse8 hours

Featured reviews

EJ

5.0Reviewed Nov 23, 2025

Thanks to this course, I can now confidently use MATLAB to solve optimization problems with evolutionary algorithms. Practical skills that immediately paid off!

QH

5.0Reviewed Nov 9, 2025

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

DD

5.0Reviewed Nov 21, 2025

This course revolutionized my approach to complex optimization problems with practical evolutionary computation techniques.

WW

5.0Reviewed Nov 20, 2025

This course demystifies evolutionary algorithms with clear explanations and practical examples, making complex optimization techniques accessible to all engineers and scientists.

MM

5.0Reviewed Dec 1, 2025

The instructor’s expertise in genetic programming and evolutionary strategies brought real world relevance to abstract concepts like multi-objective optimization.

ZZ

5.0Reviewed Nov 20, 2025

The course’s comparison of evolutionary algorithms with traditional gradient-based methods highlighted their unique advantages in non-convex and high-dimensional problems.

ZZ

5.0Reviewed Nov 21, 2025

The hands-on projects made mastering genetic algorithms feel effortless and immediately applicable to real-world engineering challenges.

MJ

5.0Reviewed Dec 1, 2025

The comparison between traditional math optimization and evolutionary computation was eye opening. It clarified why AI methods are so powerful!

SS

5.0Reviewed 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.

OH

5.0Reviewed Nov 23, 2025

This course bridged the gap between theoretical AI and practical engineering. Every module had clear, actionable takeaways!

JW

5.0Reviewed Nov 23, 2025

Finally, a course that explains why evolutionary algorithms work, not just how to use them. Deepened my understanding of AI’s engineering potential!

TT

5.0Reviewed Nov 20, 2025

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

All reviews

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