Natural Computation - CO837

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2020 to 2021
Canterbury
(version 2)
Autumn 7 15 (7.5) PROF A Freitas checkmark-circle

Overview

There is an increasing use of nature-inspired computational techniques in computer science. These include the use of biology as a source of inspiration for solving computational problems, such as developments in evolutionary algorithms and swarm intelligence. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their late careers.

Details

This module appears in the following module collections.

Contact hours

Total contact hours: 22
Private study hours: 128
Total study hours: 150

Method of assessment

13. Assessment methods
13.1 Main assessment methods
One Computational exercise 20%
One short Essay (about 1,000 words) 20%
Examination 60%

13.2 Reassessment methods
Like for like.

Indicative reading

Eiben, AE, Smith, JE. (2015) Introduction to Evolutionary Computing, 2nd Edition. Springer.
Dorigo, M. and Stutzle, T. (2004) Ant Colony Optimization, MIT Press.
Barnes, DJ, Chu, D. (2010) Introduction to Modeling for Biosciences, Springer

See the library reading list for this module (Canterbury)

Learning outcomes

8. The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
8.1 To be able to describe what is meant by a natural computation paradigm, list a number of natural computing paradigms and give a brief description of each together with some examples of their (actual or potential) applications.
8.2 To be able to select the appropriate technique for a particular problem from a set of problem-solving heuristics based on these natural computing paradigms, and to be able to justify this choice based on a knowledge of the properties and potential of these methods. To be able to compare the general capabilities of a number of such methods and give an overview of their comparative strengths and weaknesses.
8.3 To be able to analyse phenomena from the natural world from the point of view of their being computational systems. To be able to take these phenomena and distinguish between the features which are important for computational problem solving and those that are merely a fact of their realization in the natural world.
8.4 To be able to implement a natural computation system on the computer, and apply this program to the solution of problems.
8.5 To be able to exploit library and online resources to support investigations into these areas.



9. The intended generic learning outcomes.
On successfully completing the module students will be able to:
9.1 To be able to write coherently and critically about the topics studied in the course, based on readings from the scientific literature and demonstrating an awareness of how to write in a scientific manner.
9.2 To be able to apply mathematical techniques where appropriate.
9.3 To be able to apply appropriate computer programming techniques.
9.4 To be able to apply appropriate scientific principles and methodology.
9.5 To be able to study independently and apply principles and techniques used in the course to new examples.

Notes

  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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