CQUniversity Unit Profile
COIT29224 Evolutionary Computation
Evolutionary Computation
All details in this unit profile for COIT29224 have been officially approved by CQUniversity and represent a learning partnership between the University and you (our student).
The information will not be changed unless absolutely necessary and any change will be clearly indicated by an approved correction included in the profile.
General Information

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

Career Level: Postgraduate
Unit Level: Level 9
Credit Points: 6
Student Contribution Band: 8
Fraction of Full-Time Student Load: 0.125

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 3 - 2021

Online

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).

Class and Assessment Overview

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

Bundaberg, Cairns, Emerald, Gladstone, Mackay, Rockhampton, Townsville
Adelaide, Brisbane, Melbourne, Perth, Sydney

Assessment Overview

1. Practical Assessment
Weighting: 25%
2. Practical Assessment
Weighting: 35%
3. Written Assessment
Weighting: 40%

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.

Previous Student Feedback

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 Student evaluation

Feedback

Expect to see more examples for particle swarm optimisation.

Recommendation

Include more example solutions on particle swarm optimisation.

Unit Learning Outcomes
On successful completion of this unit, you will be able to:
  1. Formulate an evolutionary computation search or optimisation problem by analysing an authentic case or scenario
  2. Design an evolutionary algorithm for a problem applying the core evolutionary computation concepts and mechanisms
  3. Build a software application to implement an evolutionary algorithm for a complex search or optimisation problem
  4. 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 Learning Outcomes, Assessment and Graduate Attributes
N/A Level
Introductory Level
Intermediate Level
Graduate Level
Professional Level
Advanced Level

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

Alignment of Assessment Tasks to Graduate Attributes

Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7
1 - Practical Assessment - 25%
2 - Practical Assessment - 35%
3 - Written Assessment - 40%
Textbooks and Resources

Textbooks

Prescribed

Particle Swarm Optimization (2006)

Authors: Maurice Clerc
Wiley
ISBN: 9780470612163
Binding: eBook

Additional Textbook Information

This book is available from the library.

IT Resources

You will need access to the following IT resources:
  • CQUniversity Student Email
  • Internet
  • Unit Website (Moodle)
  • Anaconda3 2019.10 or 2020.02
  • Spyder 5.1.5 (Available from https://github.com/spyder-ide/spyder/releases/)
Referencing Style

All submissions for this unit must use the referencing styles below:

For further information, see the Assessment Tasks.

Teaching Contacts
Mary Tom Unit Coordinator
m.tom@cqu.edu.au
Schedule
Week 1 Begin Date: 08 Nov 2021

Module/Topic

Particle Swarm Optimization (PSO): Basic Algorithm and Python Implementation

Chapter

Lecture Notes and Chapter 3

Events and Submissions/Topic

Week 2 Begin Date: 15 Nov 2021

Module/Topic

PSO: Algorithmic Efficiency and Benchmarks with Python Example

Chapter

Lecture Notes and Chapters 1 and 6.

Events and Submissions/Topic

Week 3 Begin Date: 22 Nov 2021

Module/Topic

PSO: Parameter Settings

Chapter

Lecture Notes and Chapter 7

Events and Submissions/Topic

Week 4 Begin Date: 29 Nov 2021

Module/Topic

PSO: Problems and Applications - Part One

Chapter

Lecture Notes

Events and Submissions/Topic

Vacation Week Begin Date: 06 Dec 2021

Module/Topic

Chapter

Events and Submissions/Topic

Week 5 Begin Date: 13 Dec 2021

Module/Topic

PSO: Problems and Applications - Part Two

Chapter

Lecture Notes

Events and Submissions/Topic

Week 6 Begin Date: 20 Dec 2021

Module/Topic

Introduction to Evolution Strategy (ES)

Chapter

Lecture Notes

Events and Submissions/Topic

Assignmment 1 Due: Week 6 Tuesday (21 Dec 2021) 11:45 pm AEST
Vacation Week Begin Date: 27 Dec 2021

Module/Topic

Chapter

Events and Submissions/Topic

Week 7 Begin Date: 03 Jan 2022

Module/Topic

The Basic ES Algorithm

Chapter

Lecture Notes

Events and Submissions/Topic

Week 8 Begin Date: 10 Jan 2022

Module/Topic

Adaptation of Strategy Parameters and Co-variance Matrix

Chapter

Lecture Notes

Events and Submissions/Topic

Week 9 Begin Date: 17 Jan 2022

Module/Topic

Covariance Matrix Adaptation Evolution Strategy

Chapter

Lecture Notes

Events and Submissions/Topic

Week 10 Begin Date: 24 Jan 2022

Module/Topic

Introduction to Tree-based Genetic Programming

Chapter

Lecture Notes

Events and Submissions/Topic

Assignment 2 Due: Week 10 Monday (24 Jan 2022) 11:45 pm AEST
Week 11 Begin Date: 31 Jan 2022

Module/Topic

Genetic Programming Preparatory Steps

Chapter

Lecture Notes

Events and Submissions/Topic

Week 12 Begin Date: 07 Feb 2022

Module/Topic

Automatically Defined Functions

Chapter

Lecture Notes

Events and Submissions/Topic

Exam Week Begin Date: 14 Feb 2022

Module/Topic

Chapter

Events and Submissions/Topic

Assignment 3 Due: Exam Week Wednesday (16 Feb 2022) 11:45 pm AEST
Term Specific Information

Unit Coordinator
Dr. Mary Tom
College of Information and Communication Technology
School of Engineering and Technology
Central Queensland University
Brisbane QLD 4000, Australia
Phone: +61 7 3295 1119
Email: m.tom@cqu.edu.au

Assessment Tasks

1 Practical Assessment

Assessment Title
Assignmment 1

Task Description

In this assignment you will analyse the given scenario, identify the optimization problem, and the parameters required. You will model the optimization problem as a Particle Swarm Optimization (PSO). You will design and build the software solution applying PSO techniques. This assessment task is to design, code, debug, and test a software application using the topics learnt in Weeks 1 - 5. Further details are in the Assignment 1 specification document available from the Unit website.


Assessment Due Date

Week 6 Tuesday (21 Dec 2021) 11:45 pm AEST


Return Date to Students

Week 8 Tuesday (11 Jan 2022)


Weighting
25%

Assessment Criteria

  1. Clear analysis of the given scenario or case study and identify the optimization problem.
  2. Correct identification of parameters from the scenario.
  3. Development of the particle swarm optimization (PSO) model.
  4. Implementation of a working software solution for the identified problem.
  5. Concise documentation of testing, and specific aspect of PSO as required.


Referencing Style

Submission
Online

Submission Instructions
Submit one zip file containing the source code files (.py) and the report file (.doc).

Learning Outcomes Assessed
  • 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


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Self-management

2 Practical Assessment

Assessment Title
Assignment 2

Task Description

In this assignment you will use evolution strategy (ES) to model solution to a complex optimization problem. You will analyse the given scenario, and identify the  parameters and model an ES optimization technique. You will design and implement a software solution that applies evolution strategy (ES) for the identified optimization problem. This assessment task is to assess your understanding of the topics learnt in Weeks 6 - 9. Further details are in the Assignment 2 specification document available from the Unit website.


Assessment Due Date

Week 10 Monday (24 Jan 2022) 11:45 pm AEST


Return Date to Students

Week 11 Wednesday (2 Feb 2022)


Weighting
35%

Assessment Criteria

  1. Clear Analysis of the given case study or scenario and identification of the optimization problem.
  2. Correct identification of strategy parameters and modeling of the solution.
  3. Correct implementation of the software solution.
  4. Detailed comparison of PSO and ES.


Referencing Style

Submission
Online

Submission Instructions
Submit one zip file containing the source code files (.py) and the report file (.doc).

Learning Outcomes Assessed
  • 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


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Self-management
  • Leadership

3 Written Assessment

Assessment Title
Assignment 3

Task Description

In this assignment you will research on the current state of genetic programming (GP) in terms of its development and practical applications. You will report your findings concisely and clearly. You will also apply GP techniques to a regression problem. You will analyse application of optimization techniques to improve performance of a machine learning solution to a problem. You will evaluate the application and write a report describing the effectiveness.


Assessment Due Date

Exam Week Wednesday (16 Feb 2022) 11:45 pm AEST


Return Date to Students

Results will be published on certification date.


Weighting
40%

Assessment Criteria

  1. Clear analysis of the current developments in genetic programming (GP).
  2. Concise and clear reporting of contemporary developments and applications of genetic programming.
  3. Correct modeling of the regression problem and development of solution applying GP techniques.
  4. Detailed description of optimization techniques applied to a chosen case study for improving the performance of a computational intelligence solution.
  5. Concise description of the merits of performance enhancement.


Referencing Style

Submission
Online

Submission Instructions
Submit one zip file containing the source code files (.py) and the report file (.doc).

Learning Outcomes Assessed
  • Write an article that evaluates the performance and interprets the results of your software application of evolutionary computation paradigm to an authentic problem.


Graduate Attributes
  • Knowledge
  • Communication
  • Research
  • Ethical and Professional Responsibility

Academic Integrity Statement

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?