COMPUTATIONAL INTELLIGENCE - 2017/8
Module code: COM3013
JIN Y Prof (Computer Sci)
Number of Credits
FHEQ Level 6
Module cap (Maximum number of students)
Overall student workload
Independent Study Hours: 104
Lecture Hours: 24
Laboratory Hours: 22
|Assessment type||Unit of assessment||Weighting|
|Coursework||COURSEWORK: INDIVIDUAL (PROGRAMMING)||50|
|Examination||FINAL PRACTICAL EXAM (2 HOURS)||50|
Prerequisites / Co-requisites
Good skill in C/C++ programming, good knowledge in mathematics (calculus)
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.
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.
|1||Understand the main principles of computational intelligence||C|
|2||Gain hands-on knowledge and experience on designing evolutionary algorithms and neural network based learning algorithms for problem solving||K|
|3||Perform in-depth research on topics related to computational intelligence||PT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Lesson 1: Introduction
Understanding nature and solving engineering problems
Professional organizations, major journals and conferences
Lesson 2: Evolutionary Algorithms
A generic framework
Lesson 3: Swarm Intelligence
Swarm intelligence in nature
Particle swarm optimization
Lesson 4: Multi-Objective Evolutionary Algorithms
Dynamic weighted aggregation
Elitist non-dominated sorting genetic algorithms
Lesson 5: Neural Network Models
Other neural network models
Lesson 6: Learning Algorithms
Other learning schemes
Lesson 7: Hybrid Systems I
Evolutionary optimization of neural networks
Knowledge extraction from neural networks
Knowledge incorporation into neural networks
Lesson 8: Hybrid Systems II
Lesson 9: Surrogate-Assisted Evolutionary Optimization
Evolutionary computation for expensive problems
Basic model management
Advanced model management
Evolutionary optimization of aerodynamic structures
Lesson 10: Evolutionary Optimization in Uncertain Environments
Search for robust solutions
Tracking moving optima
Lesson 11: Evolutionary Developmental Systems
Gene regulatory networks
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 (week 1-11)
2-hour lab, including coursework and assignments (week 2-11)
2-hour review (week 12)
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 assignments, and then two pieces of major coursework that require programming skills and ability to solve new problems.
Thus, the summative assessment for this module consists of:
· A number of assignments will be given to the students for practice in each lecture. Minor programming tasks for using a c/c++ library will also be assigned to students for the lab session.
· Two coursework will be released to students at least 4 weeks before the submission deadline. The duration of final exam is 2 hours. The feedback on the coursework will be given to the students within two weeks after the submission deadline.
Formative assessment and feedback
· For assignments, reference solutions will be given to the students
· For coursework, feedback in terms of comments will be given to the students within 2 week time.
· A discussion of the issues will be given for each coursework.
Programmes this module appears in
|Computer Science BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Computing and Information Technology BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics and Computer Science BSc (Hons)||1||Optional||A weighted aggregate mark of 40% 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 2017/8 academic year.