Module code: COMM062

Module Overview

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed.
Good skill in Python programming, good knowledge in mathematics (calculus) are required.

Module provider

Computer Science and Electronic Eng

Module Leader

GUERIN Frank (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 76

Lecture Hours: 22

Laboratory Hours: 22

Guided Learning: 10

Captured Content: 20

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

  • Natural and Computational intelligence

  • Understanding nature and solving engineering problems

  • Formulation of an optimisation problem

  • An overview of gradient-based optimisation methods

  • The basics of metaheuristic optimisation

  • Evolutionary algorithms

    • Genetic algorithms

    • Genetic programming

    • Evolution strategies

  • Swarm intelligence in nature

    • Particle swarm optimisation

    • Differential evolution

  • Memetic algorithms

  • Multi-objective evolutionary algorithms

    • Dynamic weighted aggregation

    • Dominance-based selection

    • Elitist non-dominated sorting genetic algorithms

    • Performance measures

    • Many-objective evolutionary algorithms

  • Neural network models

    • Multi-layer perceptron networks

    • Backpropagation

    • Deep learning

    • Model selection

    • Cross validation

    • Regularisation

    • Ensemble learning

    • Pruning

    • Dropout

  • Automated machine learning

    • Evolutionary optimization of neural networks/Neural architecture search

    • Knowledge extraction from neural networks

  • Transfer learning

  • Surrogate-assisted metaheuristic Optimisation

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test Class Test 1hr 25

Alternative Assessment

Coursework assessment can be carried out individually if required.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate not only their ability to learn new knowledge, but also the ability to reuse the learned knowledge. This will be done in a step by step approach by training students for solving small, simple problems in terms of non-summative assignments, a mid-term (summative) class test, and then one piece of major coursework that requires programming skills and ability to solve new problems.

Thus, the summative assessment for this module consists of: ·

  • A mid term class test.

  • A group coursework.

The feedback on the coursework will be given to the students within three weeks after the submission deadline. ·

Formative assessment: · A number of assignments will be given to the students for practice for the mathematical aspects. Minor programming tasks for using a Python library will also be assigned to students for the lab session.

Module aims

  • The module aims to demonstrate how computing techniques can be used to understanding natural intelligence, such as evolution, learning and development. Meanwhile, the module intends to show how knowledge gained from understanding natural intelligence be effectively used for solving engineering problems. Finally, this module should arouse students' interest in researching into nature-inspired computing techniques for understanding nature and problem solving. This module also aims to train the students for doing independent research, such as doing literature search, making a research proposal and presenting research results.

Learning outcomes

Attributes Developed
001 Understand the main principles of computational intelligence C
002 Gain hands-on knowledge and experience on designing evolutionary algorithms and neural network based learning algorithms for problem solving K
003 Perform in-depth research on topics related to computational intelligence T

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to train students the ability to independently learn knowledge and solve problems by reusing learning knowledge. The module involves many real-world problems from industry on optimisation and prediction.

The learning and teaching methods include: The delivery pattern will consist of:  Lectures and Lab, including coursework and assignments

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: COMM062

Other information

Digital Capabilities
The advanced AI skills taught in this module provide students digital skills that are fundamental to solving many computer science problems today. It teaches students techniques to use computers to search large high dimensional problem spaces to find optimal solutions. It also covers a wide range of computational intelligence techniques. These skills are highly valued in industry.

This module provides advanced AI, and software skills that are important in solving a many real-life problems today. Students are equipped with practical problem-solving skills, theoretical skills, and mathematical and statistical skills, all of which are highly valuable to employers.

Global and Cultural Skills
Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world.

Resourcefulness and Resilience
This module involves practical problem-solving skills that teach a student how to reason about and solve new unseen problems through combining the theory taught with practical technologies for systems that are in everyday use. Successfully completing the coursework requires persistence to engage in the process of trial and error that is needed to explore the solution space, and to solve the sub-problems that arise along the way.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Data Science MSc 1 Optional A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.