What will I learn on this module?
Evolutionary algorithms (EAs) are a class of optimisation techniques that are inspired by natural evolution. They are well-suited to problems for which no specific solution method exists, or where other methods perform badly, and they have been used very successfully in areas as diverse as finance, engineering, architecture and logistics. In this module, you will learn about the fundamental principles of EAs, understand how (and why) they are used, and develop your own EA for a specific problem.
EAs are used extensively in business, industry, and scientific research, and the ability to analyse a problem and develop an EA to solve it demonstrates a number of sought-after key skills.
This module is research-driven, and is taught by academics who have published extensively in the field of evolutionary computation. You will benefit from access to up-to-date knowledge, codebases and datasets.
The main component of assessment is a development assignment (70%), which will bring together the skills and techniques that you will acquire during the course of the module.
Indicative list of topics:
• Natural and artificial evolution
• Representation schemes and search operators for optimisation
• Constrained and multi-objective optimisation
• Evaluation of evolutionary algorithm performance
• Theoretical foundations
• State-of-the-art applications
How will I learn on this module?
You will learn through lectures, workshops, and independent learning. The lectures will cover theories and concepts that will enable you to tackle a series of guided exercises. You will work on these during workshops and hands-on sessions in Northumbria’s CIS building computer labs, which are fully equipped with the latest industry-standard software.
How will I be supported academically on this module?
You will be supported by lecturers during the timetabled sessions, when you will receive feedback on your work. The University’s eLearning Portal offers remote access to all lecture and seminar materials to reinforce your learning. In addition, the university library offers support for all students through the provision of digital reading lists.
Specific resources to be provided include curated lists of state-of-the-art research publications, and appropriate EA libraries with which to build your own applications.
What will I be expected to read on this module?
All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.
What will I be expected to achieve?
Knowledge & Understanding:
ML01 – Demonstrate a deep and critical understanding of the features of different Evolutionary Algorithms (KU1, KU2).
Intellectual / Professional skills & abilities:
ML02 - Compare, contrast and justify the design features and implementation strategy for an Evolutionary Algorithm to solve a specific real-world problem (KU4).
ML03 – Analyse the effectiveness of an implemented Evolutionary Algorithm for a specific problem (IPSA3, IPSA4).
Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
ML04 – Practise and develop independent enquiry and research skills, as well as articulating critical thinking evidenced by demonstrating and reporting on an advanced Evolutionary Algorithm implementation. (PVA1, PVA5).
How will I be assessed?
The first element of summative assessment (30%) will be an analysis assignment.
This assessment addresses Module Learning Outcomes ML01 and ML04.
The main element of summative assessment (70%) will be an implementation and documentation assignment.
This assessment addresses Module Learning Outcomes – ML02, ML03.
On an on-going basis you will also receive formative feedback on exercises you are required to complete.
Pre-requisite(s)
N/A
Co-requisite(s)
N/A
Module abstract
This module introduces you to the theory, principles and practice of Evolutionary Algorithms (EA); optimisation methods based on the biological principle of evolution by natural selection. They are used extensively in a wide range of scientific, industrial, environmental and business applications (e.g., aircraft design, complex vehicle scheduling and routeing, and financial technology (“fintech”)), and are often used to automatically generate novel or creative solutions to complex problems such as sustainable building design. Demonstrating a firm grounding in the use of EAs is an extremely marketable skill. You will be introduced to the latest research in EAs through the use of standardised toolkits and guided reading of scientific papers. The practical assessment of the module will test your ability to analyse a problem and design, implement and evaluate an EA for its solution.
Course info
UCAS Code G407
Credits 20
Level of Study Undergraduate
Mode of Study 3 years Full Time or 4 years with a placement (sandwich)/study abroad
Department Computer and Information Sciences
Location City Campus, Northumbria University
City Newcastle
Start September 2025
All information is accurate at the time of sharing.
Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.
Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.
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