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DRPS : Course Catalogue : School of Informatics : Informatics - Distance Learning

Postgraduate Course: Natural Computing (INFD11007)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
Summary*This course replaces the course 'Natural Computing INFR11165' from 2019/20*

*Please note that this is an online learning course, aimed at students on the DSTI or Informatics distance learning programmes*.

This module teaches you about bio-inspired algorithms for optimisation and search problems. The algorithms are based on simulated evolution (including Genetic algorithms and Genetic programming), particle swarm optimisation, ant colony optimisation as well as systems made of membranes or biochemical reactions among molecules. These techniques are useful for searching very large spaces. For example, they can be used to search large parameter spaces in engineering design and spaces of possible schedules in scheduling. However, they can also be used to search for rules and rule sets, for data mining, for good feed-forward or recurrent neural nets and so on. The idea of evolving, rather than designing, algorithms and controllers is especially appealing in AI. In a similar way it is tempting to use the intrinsic dynamics of real systems consisting e.g. of quadrillions of molecules to perform computations for us. The course includes technical discussions about the applicability and a number of practical applications of the algorithms.

In this module, students will learn about

- The practicalities of natural computing methods: How to design algorithms for particular classes of problems.

- Some of the underlying theory: How such algorithms work and what is provable about them.

- Issues of experimental design: How to decide whether an metaheuristic algorithm works well.

- Current commercial applications.

- Current research directions.
Course description The lectures will cover the following subjects:

- Computational aspects of animal behaviour and of biological, chemical or physical systems
- Genetic and Evolutionary Algorithms: Selection, recombination and mutation, fitness and objective functions
- Swarm intelligence, particle swarms, differential evolution, robot swarms
- Theory: the schema theorem and its flaws; convergence, statistical mechanics approaches
- Comparisons among various metaheuristic algorithms, No-Free-Lunch theorems
- Hybrid, hyperheuristic, and memetic algorithms
- Multi-objective optimisation
- Genetic programming
- Applications such as engineering optimisation; scheduling; data-mining; neural net design
- Experimental issues: Design and analysis of sets of experiments

Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Data Structures and Algorithms, Simulation and Modelling
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Mathematical background, at the level of undergraduate informatics, particularly linear algebra (vector spaces, subspaces, eigenvalues), calculus (partial derivation, extrema, concavity) and statistics (mean and variance, hypotheses testing, principal component analysis). Some programming will be required.
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  None
Course Start Semester 1
Course Start Date 19/09/2022
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 16, Online Activities 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 72 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) The coursework consists of 10 weekly on-line quizzes, 10% in total. There is one major piece of CW requiring you to perform specified tasks using a set of algorithms, and to present the results in written reports. The report is marked and is worth 40%.

You should expect to spend approximately 25 hours on the coursework for this course.
Feedback Students will get formative feedback through weekly tests via automatic evaluation of test questions, and the VLE's online discussion forum.
The tutors will give feedback on the 5 non-assessed drill exercises. Summative feedback will occur through written feedback on the assignment and the exams. The assignment will also be prepared and evaluated in the inverse class room meetings.
Additionally, we will monitor class issues through the use of a class student representative, and also occasional SurveyMonkey (or equivalent) polls.
Engagement of students with the course will be tracked continuously and compared to patterns that are reported in the online education literature.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understanding of natural computation techniques in theory and in their broad applicability to a range of hard problems in search, optimisation and machine learning.
  2. To know when a natural computing technique is applicable, which one to choose and how to evaluate the results.
  3. To know how to apply a natural computing technique to a real problem and how to choose the parameters for optimal performance.
  4. Matching techniques with problems, evaluating results, tuning parameters, creating (memetic) algorithms by evolution.
Reading List
- Melanie Mitchell: An Introduction to Genetic Algorithms. MIT Press, 1998.
- Xin-She Yang: Nature-Inspired Metaheuristic Algorithms. Luniver, 2010.
- Brabazon, O'Neill, McGarraghy: Natural Computing Algorithms. Springer, 2015.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsOnline learning,NAT-DL,Natural Computing
Course organiserDr Michael Herrmann
Tel: (0131 6)51 7177
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
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