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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 3 modules in this course

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.

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

11 videos5 readings1 assignment1 discussion prompt

11 videosTotal 69 minutes

Module 4 Introduction to Week 4's Study1 minutePreview module
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: Concepts9 minutes
Formulate Engineering Problems to Optimization Problems: Procedures and A Case Study11 minutes
Case Studies (1)3 minutes
Case Studies (2)7 minutes
Case Studies (3)3 minutes
Case Studies (4)0 minutes

5 readingsTotal 50 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

1 assignmentTotal 30 minutes

Module 4 Quiz30 minutes

1 discussion promptTotal 10 minutes

Module 4 Discussion about Evolutionary Computation10 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 videos6 readings1 assignment1 app item1 discussion prompt

9 videosTotal 60 minutes

Module 5 Introduction to Week 5's Study1 minutePreview module
Genetic Algorithm: A First Introduction6 minutes
Genetic Algorithm: A Demo8 minutes
Genetic Algorithms: GA Representation, Initialization and Selection10 minutes
Genetic Algorithms: GA Crossover and Mutation7 minutes
Genetic Algorithms: GA Stopping Criterion and Summary5 minutes
Use GA via MATLAB: GA Function7 minutes
Use GA via MATLAB: Employ the Function6 minutes
Use GA via MATLAB: Case Studies5 minutes

6 readingsTotal 50 minutes

How to Access MATLAB Online5 minutes
Materials on Genetic Algorithms10 minutes
Materials on Genetic Algorithms10 minutes
Materials on Using Genetic Algorithms10 minutes
Materials on Case Studies10 minutes
Objective function for Assignment 55 minutes

1 assignmentTotal 30 minutes

Module 5 Quiz30 minutes

1 app itemTotal 30 minutes

Module 5 Assignment30 minutes

1 discussion promptTotal 10 minutes

Module Discussion on Genetic Algorithms10 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: