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. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)
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 2024
All information is accurate at the time of sharing.
Full time Courses starting in 2023 are primarily delivered via on-campus face to face learning but may include elements of online learning. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to flex accordingly to ensure the health and safety of our students and staff.
Contact time is subject to increase or decrease in line with additional restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors, potentially to a full online offer, should further restrictions be deemed necessary in future. Our online activity will be delivered through Blackboard Ultra, enabling collaboration, connection and engagement with materials and people.
Full time Courses starting in 2023 are primarily delivered via on-campus face to face learning but may include elements of online learning. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to flex accordingly to ensure the health and safety of our students and staff.
Contact time is subject to increase or decrease in line with additional restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors, potentially to a full online offer, should further restrictions be deemed necessary in future. Our online activity will be delivered through Blackboard Ultra, enabling collaboration, connection and engagement with materials and people.
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