Overview
Evolutionary Computation, an area of Artificial Intelligence, comprises machine learning optimisation and classification paradigms based on principles from biological sciences. In this unit, you will explore how principles from theories of evolution and natural selection can be used to construct intelligent systems. You will learn the theoretical concepts of representation, selection, reproduction, and recombination. You will apply evolutionary algorithms, such as evolution strategies, genetic programming, and particle swarm optimisation to tackle science, engineering, social, and business problems and opportunities.
Details
Pre-requisites or Co-requisites
Pre-requisite: COIT20277 Introduction to Artificial Intelligence
Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).
Offerings For Term 1 - 2026
Attendance Requirements
All on-campus students are expected to attend scheduled classes - in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).
Recommended Student Time Commitment
Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.
Class Timetable
Assessment Overview
Assessment Grading
This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of 'pass' in order to pass the unit. If any 'pass/fail' tasks are shown in the table above they must also be completed successfully ('pass' grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the 'assessment task' section (note that in some instances, the minimum mark for a task may be greater than 50%). Consult the University's Grades and Results Policy for more details of interim results and final grades.
All University policies are available on the CQUniversity Policy site.
You may wish to view these policies:
- Grades and Results Policy
- Assessment Policy and Procedure (Higher Education Coursework)
- Review of Grade Procedure
- Student Academic Integrity Policy and Procedure
- Monitoring Academic Progress (MAP) Policy and Procedure - Domestic Students
- Monitoring Academic Progress (MAP) Policy and Procedure - International Students
- Student Refund and Credit Balance Policy and Procedure
- Student Feedback - Compliments and Complaints Policy and Procedure
- Information and Communications Technology Acceptable Use Policy and Procedure
This list is not an exhaustive list of all University policies. The full list of University policies are available on the CQUniversity Policy site.
Feedback, Recommendations and Responses
Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.
Feedback from Unit Coordinator Reflection
Students would benefit from strengthening their understanding of mathematical foundations, particularly linear algebra and probability.
Introduce additional scaffolding materials or pre-assessment refreshers on linear algebra and probability to better prepare students for advanced topics such as CMA-ES and other metaheuristic algorithms.
Feedback from Unit Coordinator Reflection
Students would benefit from step-by-step examples and visual demonstrations to better connect algorithm theory with implementation.
Incorporate clear, step-by-step working examples and visual demonstrations to help students connect algorithm theory with implementation.
- Formulate an evolutionary computation search or optimisation problem by analysing an authentic case or scenario
- Design an evolutionary algorithm for a problem applying the core evolutionary computation concepts and mechanisms
- Build a software application to implement an evolutionary algorithm for a complex search or optimisation problem
- Write an article that evaluates the performance and interprets the results of your software application of evolutionary computation paradigm to an authentic problem.
The Skills Framework for the Information Age (SFIA) standard covers the skills and competencies related to information and communication technologies. SFIA defines levels of responsibility and skills. SFIA is adopted by organisations, governments and individuals in many countries. SFIA is increasingly being used when developing job descriptions and role profiles. SFIA can be used by individuals for creating personal skills profile. The Australian Computer Society (ACS) recognises the SFIA and provides MySFIA for ACS members to build a skills profile.
This unit contributes to the following workplace skills as defined by SFIA 7 (the SFIA code is included):
- Software design (SWDN)
- Programming/software development (PROG)
- Testing (TEST)
- Application Support (ASUP).
Alignment of Assessment Tasks to Learning Outcomes
| Assessment Tasks | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Practical Assessment - 25% | ||||
| 2 - Practical Assessment - 35% | ||||
| 3 - Written Assessment - 40% | ||||
Alignment of Graduate Attributes to Learning Outcomes
| Graduate Attributes | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Knowledge | ||||
| 2 - Communication | ||||
| 3 - Cognitive, technical and creative skills | ||||
| 4 - Research | ||||
| 5 - Self-management | ||||
| 6 - Ethical and Professional Responsibility | ||||
| 7 - Leadership | ||||
| 8 - First Nations Knowledges | ||||
| 9 - Aboriginal and Torres Strait Islander Cultures | ||||
Alignment of Assessment Tasks to Graduate Attributes
| Assessment Tasks | Graduate Attributes | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
| 1 - Practical Assessment - 25% | |||||||||
| 2 - Practical Assessment - 35% | |||||||||
| 3 - Written Assessment - 40% | |||||||||
Textbooks
Particle Swarm Optimization
Edition: 1st (2006)
Authors: Maurice Clerc
Wiley
ISBN: 9780470612163
Swarm Intelligence and Evolutionary Computation
Edition: 1st (2023)
Authors: Georgios Kouziokas
CRC Press
ISBN: 9781003247746
Additional Textbook Information
Anaconda (Windows/Mac/Linux):
Data Science Platform (Python programming language-based API and libraries using Jupyter Notebook), which can be download from: https://www.anaconda.com/download/
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
All submissions for this unit must use the referencing styles below:
For further information, see the Assessment Tasks.
z.wang@cqu.edu.au
Module/Topic
Week 01 Introduction
Chapter
Introduction to Deep Learning and Computer Vision
Events and Submissions/Topic
Module/Topic
Week 02 Linear and Non-linear Regression
Chapter
Linear and Non-linear Regression
Events and Submissions/Topic
Module/Topic
Week 03 Regularization for Hyperspectral Signal Processing
Chapter
Regularization for Hyperspectral Signal Processing
Events and Submissions/Topic
Module/Topic
Week 04 Optimisation Techniques for Computer Vision
Chapter
Optimisation Techniques for Computer Vision
Events and Submissions/Topic
Module/Topic
Week 05 Image Feature Extraction with 2D Convolutional Filters
Chapter
Image Feature Extraction with 2D Convolutional Filters
Events and Submissions/Topic
Module/Topic
Week 06 Image Classification
Chapter
Image Classification
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
Week 07 Object Detection and Segmentation
Chapter
Object Detection and Segmentation
Events and Submissions/Topic
Module/Topic
Week 08 Modelling Sequential Data in Computer Vision
Chapter
Modelling Sequential Data in Computer Vision
Events and Submissions/Topic
Module/Topic
Week 09 Attention and Transformer
Chapter
Attention and Transformer
Events and Submissions/Topic
Module/Topic
Week 10 Video Understanding
Chapter
Video Understanding
Events and Submissions/Topic
Module/Topic
Week 11 3D Perception and Mapping
Chapter
3D Perception and Mapping
Events and Submissions/Topic
Module/Topic
Week 12 Applied Case Study
Chapter
Applied Case Study
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
If you have any questions, please contact the unit coordinator at z.wang@cqu.edu.au
1 Practical Assessment
Assessment 1 will be an individual practical assessment which is based on the contents from weeks 1-5. Through this assessment, students will demonstrate their ability to select the appropriate optimisation algorithm to solve a real-world problem. This assessment will address the following unit learning outcome
- Formulate an evolutionary computation search or optimisation problem by analysing an authentic case or scenario
- Write a concise technical report
AI ASSESSMENT SCALE - AI PLANNING
You may use Al for planning, idea development, and research. Your final submission should show how you have developed and refined these ideas.
Week 6 Friday (17 Apr 2026) 11:45 pm AEST
Late submissions are subject to the university's late submission penalty policies.
Week 8 Monday (4 May 2026)
- Writing professional academic report
- Modularise Code
- Use of repo management and practice agile
- Evaluate and design the best optimisation technique for the problem
- Successful implementation of the optimisation technique to solve the problem
- Formulate an evolutionary computation search or optimisation problem by analysing an authentic case or scenario
- Design an evolutionary algorithm for a problem applying the core evolutionary computation concepts and mechanisms
- Build a software application to implement an evolutionary algorithm for a complex search or optimisation problem
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Self-management
2 Practical Assessment
Assessment -2 is an individual work where students have to write Python code to build the software solution using deep learning. Students have to design and build the software solution applying the deep learning technique to solve the problem(s) and have to justify the reason for choosing the specific parameters. This assessment will address the following unit learning outcome
- Design a deep learning algorithm for a problem applying the core deep learning concepts and mechanisms
AI ASSESSMENT SCALE - AI PLANNING
You may use Al for planning, idea development, and research. Your final submission should show how you have developed and refined these ideas.
Week 10 Friday (22 May 2026) 11:45 pm AEST
Late submissions are subject to the university's late submission penalty policies.
Exam Week Monday (8 June 2026)
- Build a robust solution for the optimization problem by analysing an authentic case
- Comparison with other techniques
- Unit testing
- Apply core deep learning (DL)
- Formulate an evolutionary computation search or optimisation problem by analysing an authentic case or scenario
- Design an evolutionary algorithm for a problem applying the core evolutionary computation concepts and mechanisms
- Build a software application to implement an evolutionary algorithm for a complex search or optimisation problem
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Self-management
- Leadership
3 Written Assessment
This assessment builds directly on Assessment 2. Using the experimental results obtained in Assessment 2, you are required to write a research-style journal article that presents, analyses, and discusses your mango detection work using deep learning object detection models.
You will structure your article following the IEEE Access submission template, which is provided. The article should be written in a formal academic style and present your work as if submitting to a peer-reviewed journal.
This assessment will address the following unit learning outcomes
- Write an article that evaluates the performance and interprets the results of your software application of the deep learning paradigm to an authentic problem
AI ASSESSMENT SCALE - AI PLANNING
You may use Al for planning, idea development, and research. Your final submission should show how you have developed and refined these ideas.
Week 12 Friday (5 June 2026) 11:45 pm AEST
Late submissions are subject to the university's late submission penalty policies.
On certification of grade
The students will be marked based on their ability to:
- choose appropriate optimisation techniques to solve authentic problems including social innovation challenges
- justify the reason for this choice
- develop modularise Python code
- learn to use industry tools to solve problems
- learn ethics for system development
- Write an article that evaluates the performance and interprets the results of your software application of evolutionary computation paradigm to an authentic problem.
- Knowledge
- Communication
- Research
- Ethical and Professional Responsibility
As a CQUniversity student you are expected to act honestly in all aspects of your academic work.
Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.
When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.
Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.
As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.
What is a breach of academic integrity?
A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.
Why is academic integrity important?
A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.
Where can I get assistance?
For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.
What can you do to act with integrity?
