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Advanced Intelligent Optimization and its Applications

University of Glasgow via Coursera

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Overview

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The second course introduces advanced evolutionary computation methods for intelligent optimization and their applications. This course is built on the Applied AI Foundations Specialization, which introduced the fundamentals of genetic algorithms and particle swarm optimization. In this course, the configuration of intelligent optimization algorithms, handling constraints and multiple objectives, as well as combinatorial optimization, is introduced. Since almost all design problems in science and engineering can be formulated into optimization problems, after this course, you will become a professional designer armed with AI techniques, which is a big advantage compared to traditional experience-driven manual design techniques. This skill can be transferred to various domains in science and engineering.

Syllabus

Configuration of Evolutionary Algorithms
- In the Applied AI for Engineers and Scientists: Foundations Specialization, you were introduced to intelligent optimization algorithms such as genetic algorithms and particle swarm optimization. In practical applications, the successful use of these methods depends heavily on the proper configuration of their key hyperparameters. This module examines how such parameter settings influence optimization performance and how to select them effectively in real-world science and engineering contexts. After learning this module, you will be able to:
Constrained and Multiobjective Optimization
- This module introduces the concepts of constrained and multiobjective optimization, as well as the state-of-the-art algorithms for solving them. Python implementation, assisted by an LLM, is also taught alongside real-world case studies. After learning this module, students will be able to:
Combinatorial Optimization
- This module introduces combinatorial optimization and the algorithms to solve it. Real-world case studies and Python implementation assisted by LLM are also taught. After learning this module, students will be able to: