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: 106

Lecture Hours: 22

Laboratory Hours: 22

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Lesson 1: Introduction

  • Natural intelligence

  • Computational intelligence

  • Understanding nature and solving engineering problems

  • Professional organizations, major journals and conferences

Lesson 2:

  • Evolutionary Algorithms 

  • A generic framework

  • Genetic representations

  • Genetic variations

  • Selection schemes

Lesson 3: Genetic Programming and Swarm Intelligence

  • Genetic Programming

  • Swarm intelligence in nature

  • Particle swarm optimization

  • Adaptive PSO

  • Swarm Intelligence via Social Learning

  • Evolution Strategies

Lesson 4: Multi-Objective Evolutionary Algorithms

  • Dynamic weighted aggregation

  • Dominance-based selection

  • Elitist non-dominated sorting genetic algorithms

  • Performance measures 

Lesson5: Evolutionary Multi-Objective Optimization 

  • Evolutionary Multi-Objective Optimization 

  • Many-Objectives 

  • Application Examples 

Lesson 6: Neural Network Models

  • Perceptron

  • Multi-layer perceptrons

  • Backpropagation 

  • Deep Learning 

Lesson7: Learning Algorithms and Issues 

  • Model Selection 

  • Cross Validation

  • Regularisation 

  • Ensemble Learning

  • bootstrapping 

  • Memetic Algorithms

Lesson 8: Automated Machine Learning and Transfer Learning

  • Automated Machine Learning

  • Evolutionary optimization of neural networks

  • Knowledge extraction from neural networks

  • Transfer Learning

Lesson 9: Unsupervised and Reinforcement Learning

  • Hebbian Learning

  • Self Organising Maps

  • Reinforcement Learning

Lesson 10: Fuzzy Systems and Hybrid Systems

  • Fuzzy sets and systems

  • Neuro fuzzy systems

  • Genetic fuzzy systems

Lesson 11: Surrogate-Assisted Evolutionary Optimization

  • Evolutionary computation for expensive problems

  • Basic model management

  • Advanced model management

  • Evolutionary optimization of aerodynamic structures

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test Class Test 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 group coursework. The feedback on the coursework will be given to the students within three weeks after the submission deadline. ·

A mid term class test. 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:  2-hour lectures  2-hour 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


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 2022/3 academic year.