Module title: Emergent Computing for Optimisation

SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET11508
Module leader: Emma Hart
School School of Computing
Subject area group: Software Engineering
Prerequisites

N/A

2019/0, Trimester 1, Face-to-Face, Edinburgh Napier University
Occurrence: 001
Primary mode of delivery: Face-to-Face
Location of delivery: MERCHISTON
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Emma Hart
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
Learning & Teaching methods including their alignment to LOs
Lectures will introduce the basic concepts and theory behind the algorithms (LO1 and LO2). During lab sessions, students will be required to customise algorithms and implement solutions to practical problems, and analyse the results in a scientific manner. (LO3 and LO4). At first, benchmark problems will be used, where students will be expected to compare and contrast approaches. Later, more challenging problem will require students to apply critical thought to selecting and justifying their choice of approach. The results and justification will be assessed by writing a conference-paper style report (LO5). The examination will provide a formal assessment of their theoretical understanding of the concepts and their applicability (LO1 and LO2).

Assessment (formative or summative)
Formative assessment takes place using the VLE to deliver tutorial exercises which need to be completed, each building on a particular topic and enabling the students to identify gaps in understanding of material presented in lectures Summative assessment will take place in two parts: one assessment relates to LO1 and LO2 concerning critical evaluation of the use of algorithms in problem domains. The second assessment relates to LOs 3, 4 and 5 and involves practical implementation and evaluation of an algorithm for solving a practical problem. The examination will provide a formal assessment of their theoretical understanding of the concepts and their applicability.

Formative Assessment:
The University is currently undertaking work to improve the quality of information provided on methods of assessment and feedback. Please refer to the section on Learning and Teaching Approaches above for further information about this module’s learning, teaching and assessment practices, including formative and summative approaches.

Summative Assessment:
The University is currently undertaking work to improve the quality of information provided on methods of assessment and feedback. Please refer to the section on Learning and Teaching Approaches above for further information about this module’s learning, teaching and assessment practices, including formative and summative approaches.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Practical classes and workshops 12
Face To Face Tutorial 12
Face To Face Lecture 24
Independent Learning Guided independent study 150
Face To Face Centrally Time Tabled Examination 2
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Report 20 3,4,5 7 HOURS= 15, WORDS= 0
Centrally Time Tabled Examination 40 1,2 14/15 HOURS= 2, WORDS= 0
Report 40 3, 4, 5 13 HOURS= 30, WORDS= 0
Component 1 subtotal: 60
Component 2 subtotal: 40
Module subtotal: 100

Description of module content:

The module will address:

- Why some problems are hard to solve

- Modern heuristic search methods such as Evolutionary Algorithms, including evolution strategies; meta-heuristic search techniques such as tabu-search, simulated annealing, iterated local search; ant-colony optimisation; hyper-heuristics and associated methods.

- Appropriate experimental methodologies for testing and analysing emergent computing algorithms

- Simple statistical methods for evaluating the results

Learning Outcomes for module:

Upon completion of this module you will be able to:

LO1: Critically evaluate the use of emergent computing algorithms in practical applications

LO2: Critically assess the use of different algorithms in different problem domains

LO3: Specify, customise and use appropriate algorithms for particular practical problems

LO4: Apply an appropriate scientific approach to testing and analysing emergent computing algorithms and be able to use simple statistics to evaluate the results

LO5: Present the results in an appropriate form and draw appropriate conclusions

Indicative References and Reading List - URL:
Contact your module leader